Class List

Here are the classes, structs, unions and interfaces with brief descriptions:

[detail level 12345]

▼Nboost
▼Nmlpack	Linear algebra utility functions, generally performed on matrices or vectors
►Nadaboost
CAdaBoost	The AdaBoost class
CAdaBoostModel	The model to save to disk
►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
CGivenInitialization	This initialization rule for AMF simply fills the W and H matrices with the matrices given to the constructor of this object
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
►Nann	Artificial Neural Network
CAdd	Implementation of the Add module class
CAddMerge	Implementation of the AddMerge module class
CAddVisitor	AddVisitor exposes the Add() method of the given module
CBackwardVisitor	BackwardVisitor executes the Backward() function given the input, error and delta parameter
CBaseLayer	Implementation of the base layer
CConcat	Implementation of the Concat class
CConcatPerformance	Implementation of the concat performance class
CConstant	Implementation of the constant layer
CConvolution	Implementation of the Convolution class
CDeleteVisitor	DeleteVisitor executes the destructor of the instantiated object
CDeltaVisitor	DeltaVisitor exposes the delta parameter of the given module
CDeterministicSetVisitor	DeterministicSetVisitor set the deterministic parameter given the deterministic value
CDropConnect	The DropConnect layer is a regularizer that randomly with probability ratio sets the connection values to zero and scales the remaining elements by factor 1 /(1 - ratio)
CDropout	The dropout layer is a regularizer that randomly with probability ratio sets input values to zero and scales the remaining elements by factor 1 / (1 - ratio)
CELU	The ELU activation function, defined by
CFFN	Implementation of a standard feed forward network
CFFTConvolution	Computes the two-dimensional convolution through fft
CForwardVisitor	ForwardVisitor executes the Forward() function given the input and output parameter
CFullConvolution
CGlimpse	The glimpse layer returns a retina-like representation (down-scaled cropped images) of increasing scale around a given location in a given image
CGradientSetVisitor	GradientSetVisitor update the gradient parameter given the gradient set
CGradientUpdateVisitor	GradientUpdateVisitor update the gradient parameter given the gradient set
CGradientVisitor	SearchModeVisitor executes the Gradient() method of the given module using the input and delta parameter
CGradientZeroVisitor
CHardTanH	The Hard Tanh activation function, defined by
CIdentityFunction	The identity function, defined by
CJoin	Implementation of the Join module class
CKathirvalavakumarSubavathiInitialization	This class is used to initialize the weight matrix with the method proposed by T
CLayerTraits	This is a template class that can provide information about various layers
CLeakyReLU	The LeakyReLU activation function, defined by
CLinear	Implementation of the Linear layer class
CLinearNoBias	Implementation of the LinearNoBias class
CLoadOutputParameterVisitor	LoadOutputParameterVisitor restores the output parameter using the given parameter set
CLogisticFunction	The logistic function, defined by
CLogSoftMax	Implementation of the log softmax layer
CLookup	Implementation of the Lookup class
CLSTM	An implementation of a lstm network layer
CMaxPooling	Implementation of the MaxPooling layer
CMaxPoolingRule
CMeanPooling	Implementation of the MeanPooling
CMeanPoolingRule
CMeanSquaredError	The mean squared error performance function measures the network's performance according to the mean of squared errors
CMultiplyConstant	Implementation of the multiply constant layer
CNaiveConvolution	Computes the two-dimensional convolution
CNegativeLogLikelihood	Implementation of the negative log likelihood layer
CNguyenWidrowInitialization	This class is used to initialize the weight matrix with the Nguyen-Widrow method
COivsInitialization	This class is used to initialize the weight matrix with the oivs method
COrthogonalInitialization	This class is used to initialize the weight matrix with the orthogonal matrix initialization
COutputHeightVisitor	OutputWidthVisitor exposes the OutputHeight() method of the given module
COutputParameterVisitor	OutputParameterVisitor exposes the output parameter of the given module
COutputWidthVisitor	OutputWidthVisitor exposes the OutputWidth() method of the given module
CParametersSetVisitor	ParametersSetVisitor update the parameters set using the given matrix
CParametersVisitor	ParametersVisitor exposes the parameters set of the given module and stores the parameters set into the given matrix
CPReLU	The PReLU activation function, defined by (where alpha is trainable)
CRandomInitialization	This class is used to initialize randomly the weight matrix
CRectifierFunction	The rectifier function, defined by
CRecurrent	Implementation of the RecurrentLayer class
CRecurrentAttention	This class implements the Recurrent Model for Visual Attention, using a variety of possible layer implementations
CReinforceNormal	Implementation of the reinforce normal layer
CResetVisitor	ResetVisitor executes the Reset() function
CRewardSetVisitor	RewardSetVisitor set the reward parameter given the reward value
CRNN	Implementation of a standard recurrent neural network container
CSaveOutputParameterVisitor	SaveOutputParameterVisitor saves the output parameter into the given parameter set
CSelect	The select module selects the specified column from a given input matrix
CSequential	Implementation of the Sequential class
CSetInputHeightVisitor	SetInputHeightVisitor updates the input height parameter with the given input height
CSetInputWidthVisitor	SetInputWidthVisitor updates the input width parameter with the given input width
CSoftplusFunction	The softplus function, defined by
CSoftsignFunction	The softsign function, defined by
CSVDConvolution	Computes the two-dimensional convolution using singular value decomposition
CTanhFunction	The tanh function, defined by
CValidConvolution
CVRClassReward	Implementation of the variance reduced classification reinforcement layer
CWeightSetVisitor	WeightSetVisitor update the module parameters given the parameters set
CWeightSizeVisitor	WeightSizeVisitor returns the number of weights of the given module
CZeroInitialization	This class is used to initialize randomly the weight matrix
►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< MetricType, VecType > >	A specialization of BoundTraits for this bound type
CBoundTraits< CellBound< MetricType, ElemType > >
CBoundTraits< HollowBallBound< MetricType, ElemType > >	A specialization of BoundTraits for this bound type
CBoundTraits< HRectBound< MetricType, ElemType > >
CCellBound	The CellBound class describes a bound that consists of a number of hyperrectangles
CHollowBallBound	Hollow ball bound encloses a set of points at a specific distance (radius) from a specific point (center) except points at a specific distance from another point (the center of the hole)
CHRectBound	Hyper-rectangle bound for an L-metric
►Ncf	Collaborative filtering
►CCF	This class implements Collaborative Filtering (CF)
CCandidateCmp	Compare two candidates based on the value
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
CCustomImputation	A simple custom imputation class
CDatasetMapper	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
CImputer	Given a dataset of a particular datatype, replace user-specified missing value with a variable dependent on the StrategyType and MapperType
CIncrementPolicy	IncrementPolicy is used as a helper class for DatasetMapper
CListwiseDeletion	A complete-case analysis to remove the values containing mappedValue
CMeanImputation	A simple mean imputation class
CMedianImputation	This is a class implementation of simple median imputation
CMissingPolicy	MissingPolicy is used as a helper class for DatasetMapper
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
►Ndbscan
CDBSCAN	DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is a clustering technique described in the following paper:
CRandomPointSelection	This class can be used to randomly select the next point to use for DBSCAN
►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
CGammaDistribution	This class represents the Gamma distribution
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
CCandidateCmp	Compare two candidates based on the value
CFastMKSModel	A utility struct to contain all the possible FastMKS models, for use by the mlpack_fastmks program
►CFastMKSRules	The FastMKSRules class is a template helper class used by FastMKS class when performing exact max-kernel search
CCandidateCmp	Compare two candidates based on the value
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
CHMMModel	A serializable HMM model that also stores the type
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
CKillEmptyClusters	Policy which allows K-Means to "kill" empty clusters without any error being reported
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
CSampleInitialization
►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
►Nmvu
CMVU	Meant to provide a good abstraction for users
►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
CBiSearchVisitor	BiSearchVisitor executes a bichromatic neighbor search on the given NSType
CDeleteVisitor	DeleteVisitor deletes the given NSType instance
CDrusillaSelect
CEpsilonVisitor	EpsilonVisitor exposes the Epsilon method of the given NSType
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
CCandidateCmp	Compare two candidates based on the distance
CMonoSearchVisitor	MonoSearchVisitor executes a monochromatic neighbor search on the given NSType
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	The NeighborSearchRules class is a template helper class used by NeighborSearch class when performing distance-based neighbor searches
CCandidateCmp	Compare two candidates based on the distance
CNeighborSearchStat	Extra data for each node in the tree
CNSModel	The NSModel class provides an easy way to serialize a model, abstracts away the different types of trees, and also reflects the NeighborSearch API
CNSModelName
CNSModelName< FurthestNeighborSort >
CNSModelName< NearestNeighborSort >
CQDAFN
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	The RASearchRules class is a template helper class used by RASearch class when performing rank-approximate search via random-sampling
CCandidateCmp	Compare two candidates based on the distance
CRAUtil
CReferenceSetVisitor	ReferenceSetVisitor exposes the referenceSet of the given NSType
CSearchModeVisitor	SearchModeVisitor exposes the SearchMode() method of the given NSType
CTrainVisitor	TrainVisitor sets the reference set to a new reference set on the given NSType
►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
CGDTestFunction	Very, very simple test function which is the composite of three other functions
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]
CAdaDelta	Adadelta is an optimizer that uses two ideas to improve upon the two main drawbacks of the Adagrad method:
CAdam	Adam is an optimizer that computes individual adaptive learning rates for different parameters from estimates of first and second moments of the gradients
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
CGradientDescent	Gradient Descent is a technique to minimize a function
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
CMiniBatchSGD	Mini-batch Stochastic Gradient Descent is a technique for minimizing a function which can be expressed as a sum of other functions
CPrimalDualSolver	Interface to a primal dual interior point solver
CRMSprop	RMSprop is an optimizer that utilizes the magnitude of recent gradients to normalize the gradients
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
CExactSVDPolicy	Implementation of the exact SVD policy
CPCAType	This class implements principal components analysis (PCA)
CQUICSVDPolicy	Implementation of the QUIC-SVD policy
CRandomizedSVDPolicy	Implementation of the randomized SVD policy
►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
CBiSearchVisitor	BiSearchVisitor executes a bichromatic range search on the given RSType
CDeleteVisitor	DeleteVisitor deletes the given RSType instance
CMonoSearchVisitor	MonoSearchVisitor executes a monochromatic range search on the given RSType
CNaiveVisitor	NaiveVisitor exposes the Naive() method of the given RSType
CRangeSearch	The RangeSearch class is a template class for performing range searches
CRangeSearchRules	The RangeSearchRules class is a template helper class used by RangeSearch class when performing range searches
CRangeSearchStat	Statistic class for RangeSearch, to be set to the StatisticType of the tree type that range search is being performed with
CReferenceSetVisitor	ReferenceSetVisitor exposes the referenceSet of the given RSType
CRSModel
CRSModelName
CSerializeVisitor	Exposes the seralize method of the given RSType
CSingleModeVisitor	SingleModeVisitor exposes the SingleMode() method of the given RSType
CTrainVisitor	TrainVisitor sets the reference set to a new reference set on the given RSType
►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)
CRandomizedSVD	Randomized SVD is a matrix factorization that is based on randomized matrix approximation techniques, developed in in "Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions"
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
►CAllCategoricalSplit	The AllCategoricalSplit is a splitting function that will split categorical features into many children: one child for each category
CAuxiliarySplitInfo
CAxisParallelProjVector	AxisParallelProjVector defines an axis-parallel projection vector
►CBestBinaryNumericSplit	The BestBinaryNumericSplit is a splitting function for decision trees that will exhaustively search a numeric dimension for the best binary split
CAuxiliarySplitInfo
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
►CDualTreeTraverser	A dual-tree cover tree traverser; see dual_tree_traverser.hpp
CDualCoverTreeMapEntry	Struct used for traversal
CSingleTreeTraverser	A single-tree cover tree traverser; see single_tree_traverser.hpp for implementation
CDecisionTree	This class implements a generic decision tree learner
CDiscreteHilbertValue	The DiscreteHilbertValue class stores Hilbert values for all of the points in a RectangleTree node, and calculates Hilbert values for new points
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
CGiniGain	The Gini gain, a measure of set purity usable as a fitness function (FitnessFunction) for decision trees
CGiniImpurity
CGreedySingleTreeTraverser
CHilbertRTreeAuxiliaryInformation
CHilbertRTreeDescentHeuristic	This class chooses the best child of a node in a Hilbert R tree when inserting a new point
CHilbertRTreeSplit	The splitting procedure for the Hilbert R tree
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
CHoeffdingTreeModel	This class is a serializable Hoeffding tree model that can hold four different types of Hoeffding trees
CHyperplaneBase	HyperplaneBase defines a splitting hyperplane based on a projection vector and projection value
CInformationGain	The standard information gain criterion, used for calculating gain in decision trees
CIsSpillTree
CIsSpillTree< tree::SpillTree< MetricType, StatisticType, MatType, HyperplaneType, SplitType > >
CMeanSpaceSplit
►CMeanSplit	A binary space partitioning tree node is split into its left and right child
CSplitInfo	An information about the partition
CMidpointSpaceSplit
►CMidpointSplit	A binary space partitioning tree node is split into its left and right child
CSplitInfo	A struct that contains an information about the split
►CMinimalCoverageSweep	The MinimalCoverageSweep class finds a partition along which we can split a node according to the coverage of two resulting nodes
CSweepCost	A struct that provides the type of the sweep cost
►CMinimalSplitsNumberSweep	The MinimalSplitsNumberSweep class finds a partition along which we can split a node according to the number of required splits of the node
CSweepCost	A struct that provides the type of the sweep cost
CNoAuxiliaryInformation
CNumericSplitInfo
►COctree
CDualTreeTraverser	A dual-tree traverser; see dual_tree_traverser.hpp
CSingleTreeTraverser	A single-tree traverser; see single_tree_traverser.hpp
CSplitInfo	This is used for sorting points while splitting
CProjVector	ProjVector defines a general projection vector (not necessarily axis-parallel)
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
CRPlusPlusTreeAuxiliaryInformation
CRPlusPlusTreeDescentHeuristic
CRPlusPlusTreeSplitPolicy	The RPlusPlusTreeSplitPolicy helps to determine the subtree into which we should insert a child of an intermediate node that is being split
CRPlusTreeDescentHeuristic
CRPlusTreeSplit	The RPlusTreeSplit class performs the split process of a node on overflow
CRPlusTreeSplitPolicy	The RPlusPlusTreeSplitPolicy helps to determine the subtree into which we should insert a child of an intermediate node that is being split
►CRPTreeMaxSplit	This class splits a node by a random hyperplane
CSplitInfo	An information about the partition
►CRPTreeMeanSplit	This class splits a binary space tree
CSplitInfo	An information about the partition
CRStarTreeDescentHeuristic	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
CRStarTreeSplit	A Rectangle Tree has new points inserted at the bottom
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
CSpaceSplit
►CSpillTree	A hybrid spill tree is a variant of binary space trees in which the children of a node can "spill over" each other, and contain shared datapoints
CSpillDualTreeTraverser	A generic dual-tree traverser for hybrid spill trees; see spill_dual_tree_traverser.hpp for implementation
CSpillSingleTreeTraverser	A generic single-tree traverser for hybrid spill trees; see spill_single_tree_traverser.hpp for implementation
CTraversalInfo	The TraversalInfo class holds traversal information which is used in dual-tree (and single-tree) traversals
CTreeTraits	The TreeTraits class provides compile-time information on the characteristics of a given tree type
CTreeTraits< BinarySpaceTree< MetricType, StatisticType, MatType, bound::BallBound, SplitType > >	This is a specialization of the TreeType class to the BallTree tree type
CTreeTraits< BinarySpaceTree< MetricType, StatisticType, MatType, bound::CellBound, SplitType > >	This is a specialization of the TreeType class to the UBTree tree type
CTreeTraits< BinarySpaceTree< MetricType, StatisticType, MatType, bound::HollowBallBound, SplitType > >	This is a specialization of the TreeType class to an arbitrary tree with HollowBallBound (currently only the vantage point tree is supported)
CTreeTraits< BinarySpaceTree< MetricType, StatisticType, MatType, BoundType, RPTreeMaxSplit > >	This is a specialization of the TreeType class to the max-split random projection tree
CTreeTraits< BinarySpaceTree< MetricType, StatisticType, MatType, BoundType, RPTreeMeanSplit > >	This is a specialization of the TreeType class to the mean-split random projection tree
CTreeTraits< BinarySpaceTree< MetricType, StatisticType, MatType, BoundType, SplitType > >	This is a specialization of the TreeTraits class to the BinarySpaceTree tree type
CTreeTraits< CoverTree< MetricType, StatisticType, MatType, RootPointPolicy > >	The specialization of the TreeTraits class for the CoverTree tree type
CTreeTraits< Octree< MetricType, StatisticType, MatType > >	This is a specialization of the TreeTraits class to the Octree tree type
CTreeTraits< RectangleTree< MetricType, StatisticType, MatType, RPlusTreeSplit< SplitPolicyType, SweepType >, DescentType, AuxiliaryInformationType > >	Since the R+/R++ tree can not have overlapping children, we should define traits for the R+/R++ tree
CTreeTraits< RectangleTree< MetricType, StatisticType, MatType, SplitType, DescentType, AuxiliaryInformationType > >	This is a specialization of the TreeType class to the RectangleTree tree type
CTreeTraits< SpillTree< MetricType, StatisticType, MatType, HyperplaneType, SplitType > >	This is a specialization of the TreeType class to the SpillTree tree type
CUBTreeSplit	Split a node into two parts according to the median address of points contained in the node
►CVantagePointSplit	The class splits a binary space partitioning tree node according to the median distance to the vantage point
CSplitInfo	A struct that contains an information about the split
►CXTreeAuxiliaryInformation	The XTreeAuxiliaryInformation class provides information specific to X trees for each node in a RectangleTree
CSplitHistoryStruct	The X tree requires that the tree records it's "split history"
CXTreeSplit	A Rectangle Tree has new points inserted at the bottom
►Nutil
CCLIDeleter	Extremely simple class whose only job is to delete the existing CLI object at the end of execution
CIsStdVector	Metaprogramming structure for vector detection
CIsStdVector< std::vector< T, A > >	Metaprogramming structure for vector detection
CNullOutStream	Used for Log::Debug when not compiled with debugging symbols
COption	A static object whose constructor registers a parameter with the CLI class
CParamData	This structure holds all of the information about a single parameter, including its value (which is set when ParseCommandLine() is called)
CParameterType	Utility struct to return the type that boost::program_options should accept for a given input type
CParameterType< arma::Mat< eT > >	For matrix types, boost::program_options will accept a std::string, not an arma::mat (since it is not clear how to specify a matrix on the command-line)
CParameterType< std::tuple< mlpack::data::DatasetMapper< PolicyType >, arma::Mat< eT > > >	For matrix+dataset info types, we should accept a std::string
CParameterTypeDeducer
CParameterTypeDeducer< true, T >
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
►CBacktrace	Provides a backtrace
CFrames	Backtrace datastructure
CCLI	Parses the command line for parameters and holds user-specified parameters
CLog	Provides a convenient way to give formatted output
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 > >