| CAugLagrangian< mlpack::optimization::LRSDPFunction< mlpack::optimization::SDP< arma::sp_mat > > > | |
| CAugLagrangian< mlpack::optimization::LRSDPFunction< SDPType > > | |
| CAugLagrangianFunction< mlpack::optimization::LRSDPFunction< mlpack::optimization::SDP< arma::sp_mat > > > | |
| CAugLagrangianFunction< mlpack::optimization::LRSDPFunction< SDPType > > | |
| ▶Cstatic_visitor | |
| CBiSearchVisitor< SortPolicy > | BiSearchVisitor executes a bichromatic neighbor search on the given NSType |
| CDeleteVisitor | DeleteVisitor deletes the given NSType instance |
| CEpsilonVisitor | EpsilonVisitor exposes the Epsilon method of the given NSType |
| CMonoSearchVisitor | MonoSearchVisitor executes a monochromatic neighbor search on the given NSType |
| CReferenceSetVisitor | ReferenceSetVisitor exposes the referenceSet of the given NSType |
| CSearchModeVisitor | SearchModeVisitor exposes the SearchMode() method of the given NSType |
| CSetSearchModeVisitor | SetSearchModeVisitor modifies the SearchMode method of the given NSType |
| CTrainVisitor< SortPolicy > | TrainVisitor sets the reference set to a new reference set on the given NSType |
| 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 |
| CReferenceSetVisitor | ReferenceSetVisitor exposes the referenceSet of the given RSType |
| CSerializeVisitor< Archive > | 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 |
| ▶Ctemplate AuxiliarySplitInfo< ElemType > | |
| CDecisionTree< FitnessFunction, NumericSplitType, CategoricalSplitType, ElemType, NoRecursion > | This class implements a generic decision tree learner |
| CFastMKS< mlpack::kernel::CosineDistance > | |
| CFastMKS< mlpack::kernel::EpanechnikovKernel > | |
| CFastMKS< mlpack::kernel::GaussianKernel > | |
| CFastMKS< mlpack::kernel::HyperbolicTangentKernel > | |
| CFastMKS< mlpack::kernel::LinearKernel > | |
| CFastMKS< mlpack::kernel::PolynomialKernel > | |
| CFastMKS< mlpack::kernel::TriangularKernel > | |
| ▶CHMM< distribution::RegressionDistribution > | |
| CHMMRegression | A class that represents a Hidden Markov Model Regression (HMMR) |
| CHRectBound< metric::EuclideanDistance, ElemType > | |
| CHRectBound< MetricType > | |
| CHRectBound< mlpack::metric::LMetric, ElemType > | |
| CIPMetric< mlpack::kernel::CosineDistance > | |
| CIPMetric< mlpack::kernel::EpanechnikovKernel > | |
| CIPMetric< mlpack::kernel::GaussianKernel > | |
| CIPMetric< mlpack::kernel::HyperbolicTangentKernel > | |
| CIPMetric< mlpack::kernel::LinearKernel > | |
| CIPMetric< mlpack::kernel::PolynomialKernel > | |
| CIPMetric< mlpack::kernel::TriangularKernel > | |
| CIsVector< VecType > | 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 > > | |
| CL_BFGS< AugLagrangianFunction< LagrangianFunction > > | |
| CL_BFGS< AugLagrangianFunction< mlpack::optimization::LRSDPFunction< mlpack::optimization::SDP< arma::sp_mat > > > > | |
| CL_BFGS< AugLagrangianFunction< mlpack::optimization::LRSDPFunction< SDPType > > > | |
| CLRSDP< mlpack::optimization::SDP< arma::sp_mat > > | |
| CLRSDPFunction< mlpack::optimization::SDP< arma::sp_mat > > | |
| CAdaBoost< WeakLearnerType, MatType > | The AdaBoost class |
| CAMF< TerminationPolicyType, InitializationRuleType, UpdateRuleType > | 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< TerminationPolicy > | 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< TerminationPolicy > | 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< columnsToAverage > | 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< MatType > | This class implements residue tolerance termination policy |
| CSVDBatchLearning | This class implements SVD batch learning with momentum |
| CSVDCompleteIncrementalLearning< MatType > | 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< MatType > | This class implements validation termination policy based on RMSE index |
| CRandomInitialization | This class is used to initialize randomly the weight matrix |
| CBacktrace | Provides a backtrace |
| CBallBound< MetricType, VecType > | Ball bound encloses a set of points at a specific distance (radius) from a specific point (center) |
| CBoundTraits< BoundType > | 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< MetricType, ElemType > | The CellBound class describes a bound that consists of a number of hyperrectangles |
| CHollowBallBound< TMetricType, ElemType > | 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< MetricType, ElemType > | Hyper-rectangle bound for an L-metric |
| CIsLMetric< MetricType > | 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 |
| CCF | This class implements Collaborative Filtering (CF) |
| CDummyClass | This class acts as a dummy class for passing as template parameter |
| CFactorizerTraits< FactorizerType > | Template class for factorizer traits |
| CFactorizerTraits< mlpack::svd::RegularizedSVD<> > | Factorizer traits of Regularized SVD |
| CSVDWrapper< Factorizer > | This class acts as the wrapper for all SVD factorizers which are incompatible with CF module |
| CCLI | Parses the command line for parameters and holds user-specified parameters |
| CCustomImputation< T > | A simple custom imputation class |
| CDatasetMapper< PolicyType > | Auxiliary information for a dataset, including mappings to/from strings and the datatype of each dimension |
| CFirstArrayShim< T > | A first shim for arrays |
| CFirstNormalArrayShim< T > | A first shim for arrays without a Serialize() method |
| CFirstShim< T > | The first shim: simply holds the object and its name |
| CHasSerialize< T > | |
| CHasSerialize< T >::check< U, V, W > | |
| CHasSerializeFunction< T > | |
| CImputer< T, MapperType, StrategyType > | 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< T > | A complete-case analysis to remove the values containing mappedValue |
| CLoadCSV | Load the csv file.This class use boost::spirit to implement the parser, please refer to following link http://theboostcpplibraries.com/boost.spirit for quick review |
| CMeanImputation< T > | A simple mean imputation class |
| CMedianImputation< T > | This is a class implementation of simple median imputation |
| CMissingPolicy | MissingPolicy is used as a helper class for DatasetMapper |
| CSecondArrayShim< T > | 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< T > | A shim for objects in an array which do not have a Serialize() function |
| CSecondShim< T > | 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 |
| CDBSCAN< RangeSearchType, PointSelectionPolicy > | 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 |
| CDecisionStump< MatType > | This class implements a decision stump |
| CDTree | A density estimation tree is similar to both a decision tree and a space partitioning tree (like a kd-tree) |
| 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 |
| CDTBRules< MetricType, TreeType > | |
| 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< MetricType, MatType, TreeType > | Performs the MST calculation using the Dual-Tree Boruvka algorithm, using any type of tree |
| CEdgePair | An edge pair is simply two indices and a distance |
| CUnionFind | A Union-Find data structure |
| CFastMKS< KernelType, MatType, TreeType > | 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< KernelType, TreeType > | The FastMKSRules class is a template helper class used by FastMKS class when performing exact max-kernel search |
| CFastMKSStat | The statistic used in trees with FastMKS |
| 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< InitialClusteringType, CovarianceConstraintPolicy > | 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 |
| CHMM< Distribution > | A class that represents a Hidden Markov Model with an arbitrary type of emission distribution |
| 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< KernelType > | 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< ClusteringType, maxIterations > | Implementation of the kmeans sampling scheme |
| CLaplacianKernel | The standard Laplacian kernel |
| CLinearKernel | The simple linear kernel (dot product) |
| CNystroemMethod< KernelType, PointSelectionPolicy > | |
| 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 |
| CAllowEmptyClusters | Policy which allows K-Means to create empty clusters without any error being reported |
| CDualTreeKMeans< MetricType, MatType, TreeType > | 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< MetricType, TreeType > | |
| CElkanKMeans< MetricType, MatType > | |
| CHamerlyKMeans< MetricType, MatType > | |
| CKillEmptyClusters | Policy which allows K-Means to "kill" empty clusters without any error being reported |
| CKMeans< MetricType, InitialPartitionPolicy, EmptyClusterPolicy, LloydStepType, MatType > | 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< MetricType, MatType > | This is an implementation of a single iteration of Lloyd's algorithm for k-means |
| CPellegMooreKMeans< MetricType, MatType > | An implementation of Pelleg-Moore's 'blacklist' algorithm for k-means clustering |
| CPellegMooreKMeansRules< MetricType, TreeType > | 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 | |
| CKernelPCA< KernelType, KernelRule > | This class performs kernel principal components analysis (Kernel PCA), for a given kernel |
| CNaiveKernelRule< KernelType > | |
| CNystroemKernelRule< KernelType, PointSelectionPolicy > | |
| 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 |
| CLog | Provides a convenient way to give formatted output |
| CColumnsToBlocks | Transform the columns of the given matrix into a block format |
| CRangeType< T > | Simple real-valued range |
| CMatrixCompletion | This class implements the popular nuclear norm minimization heuristic for matrix completion problems |
| CMeanShift< UseKernel, KernelType, MatType > | This class implements mean shift clustering |
| CIPMetric< KernelType > | 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< TPower, TTakeRoot > | The L_p metric for arbitrary integer p, with an option to take the root |
| CMahalanobisDistance< TakeRoot > | The Mahalanobis distance, which is essentially a stretched Euclidean distance |
| CNaiveBayesClassifier< MatType > | The simple Naive Bayes classifier |
| CNCA< MetricType, OptimizerType > | An implementation of Neighborhood Components Analysis, both a linear dimensionality reduction technique and a distance learning technique |
| CSoftmaxErrorFunction< MetricType > | The "softmax" stochastic neighbor assignment probability function |
| CDrusillaSelect< MatType > | |
| CFurthestNeighborSort | This class implements the necessary methods for the SortPolicy template parameter of the NeighborSearch class |
| CLSHSearch< SortPolicy > | 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< SortPolicy, MetricType, MatType, TreeType, DualTreeTraversalType, SingleTreeTraversalType > | The NeighborSearch class is a template class for performing distance-based neighbor searches |
| CNeighborSearchRules< SortPolicy, MetricType, TreeType > | The NeighborSearchRules class is a template helper class used by NeighborSearch class when performing distance-based neighbor searches |
| CNeighborSearchRules< SortPolicy, MetricType, TreeType >::CandidateCmp | Compare two candidates based on the distance |
| CNeighborSearchStat< SortPolicy > | Extra data for each node in the tree |
| CNSModel< SortPolicy > | 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< SortPolicy > | |
| CNSModelName< FurthestNeighborSort > | |
| CNSModelName< NearestNeighborSort > | |
| CQDAFN< MatType > | |
| CRAModel< SortPolicy > | 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< SortPolicy > | Extra data for each node in the tree |
| CRASearch< SortPolicy, MetricType, MatType, TreeType > | The RASearch class: This class provides a generic manner to perform rank-approximate search via random-sampling |
| CRASearchRules< SortPolicy, MetricType, TreeType > | The RASearchRules class is a template helper class used by RASearch class when performing rank-approximate search via random-sampling |
| CRAUtil | |
| CSparseAutoencoder< OptimizerType > | 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 |
| CAdaDelta< DecomposableFunctionType > | Adadelta is an optimizer that uses two ideas to improve upon the two main drawbacks of the Adagrad method: |
| CAdam< DecomposableFunctionType > | Adam is an optimizer that computes individual adaptive learning rates for different parameters from estimates of first and second moments of the gradients |
| CAugLagrangian< LagrangianFunction > | The AugLagrangian class implements the Augmented Lagrangian method of optimization |
| CAugLagrangianFunction< LagrangianFunction > | 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< FunctionType > | Gradient Descent is a technique to minimize a function |
| CL_BFGS< FunctionType > | 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< SDPType > | LRSDP is the implementation of Monteiro and Burer's formulation of low-rank semidefinite programs (LR-SDP) |
| CLRSDPFunction< SDPType > | The objective function that LRSDP is trying to optimize |
| CMiniBatchSGD< DecomposableFunctionType > | Mini-batch Stochastic Gradient Descent is a technique for minimizing a function which can be expressed as a sum of other functions |
| CPrimalDualSolver< SDPType > | Interface to a primal dual interior point solver |
| CRMSprop< DecomposableFunctionType > | RMSprop is an optimizer that utilizes the magnitude of recent gradients to normalize the gradients |
| CSA< FunctionType, CoolingScheduleType > | 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< ObjectiveMatrixType > | Specify an SDP in primal form |
| CSGD< DecomposableFunctionType > | Stochastic Gradient Descent is a technique for minimizing a function which can be expressed as a sum of other functions |
| 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] |
| CParamData | Aids in the extensibility of CLI by focusing potential changes into one structure |
| CExactSVDPolicy | Implementation of the exact SVD policy |
| CPCAType< DecompositionPolicy > | This class implements principal components analysis (PCA) |
| CQUICSVDPolicy | Implementation of the QUIC-SVD policy |
| CRandomizedSVDPolicy | Implementation of the randomized SVD policy |
| CPerceptron< LearnPolicy, WeightInitializationPolicy, MatType > | 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 |
| CRadical | An implementation of RADICAL, an algorithm for independent component analysis (ICA) |
| CRangeSearch< MetricType, MatType, TreeType > | The RangeSearch class is a template class for performing range searches |
| CRangeSearchRules< MetricType, TreeType > | 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 |
| CRSModel | |
| CRSModelName | |
| 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< MatType > | The LogisticRegression class implements an L2-regularized logistic regression model, and supports training with multiple optimizers and classification |
| CLogisticRegressionFunction< MatType > | The log-likelihood function for the logistic regression objective function |
| CSoftmaxRegression< OptimizerType > | Softmax Regression is a classifier which can be used for classification when the data available can take two or more class values |
| CSoftmaxRegressionFunction | |
| 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) |
| 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< OptimizerType > | 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 | |
| CTimer | The timer class provides a way for mlpack methods to be timed |
| CTimers | |
| CAllCategoricalSplit< FitnessFunction > | The AllCategoricalSplit is a splitting function that will split categorical features into many children: one child for each category |
| CAllCategoricalSplit< FitnessFunction >::AuxiliarySplitInfo< ElemType > | |
| CAxisParallelProjVector | AxisParallelProjVector defines an axis-parallel projection vector |
| CBestBinaryNumericSplit< FitnessFunction > | The BestBinaryNumericSplit is a splitting function for decision trees that will exhaustively search a numeric dimension for the best binary split |
| CBestBinaryNumericSplit< FitnessFunction >::AuxiliarySplitInfo< ElemType > | |
| CBinaryNumericSplit< FitnessFunction, ObservationType > | The BinaryNumericSplit class implements the numeric feature splitting strategy devised by Gama, Rocha, and Medas in the following paper: |
| CBinaryNumericSplitInfo< ObservationType > | |
| CBinarySpaceTree< MetricType, StatisticType, MatType, BoundType, SplitType > | A binary space partitioning tree, such as a KD-tree or a ball tree |
| CBinarySpaceTree< MetricType, StatisticType, MatType, BoundType, SplitType >::BreadthFirstDualTreeTraverser< RuleType > | |
| CBinarySpaceTree< MetricType, StatisticType, MatType, BoundType, SplitType >::DualTreeTraverser< RuleType > | A dual-tree traverser for binary space trees; see dual_tree_traverser.hpp |
| CBinarySpaceTree< MetricType, StatisticType, MatType, BoundType, SplitType >::SingleTreeTraverser< RuleType > | A single-tree traverser for binary space trees; see single_tree_traverser.hpp for implementation |
| CCategoricalSplitInfo | |
| CCompareCosineNode | |
| CCosineTree | |
| CCoverTree< MetricType, StatisticType, MatType, RootPointPolicy > | A cover tree is a tree specifically designed to speed up nearest-neighbor computation in high-dimensional spaces |
| CCoverTree< MetricType, StatisticType, MatType, RootPointPolicy >::DualTreeTraverser< RuleType > | A dual-tree cover tree traverser; see dual_tree_traverser.hpp |
| CCoverTree< MetricType, StatisticType, MatType, RootPointPolicy >::SingleTreeTraverser< RuleType > | A single-tree cover tree traverser; see single_tree_traverser.hpp for implementation |
| CDiscreteHilbertValue< TreeElemType > | 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< MetricType, StatisticType, MatType > | 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< TreeType, RuleType > | |
| CHilbertRTreeAuxiliaryInformation< TreeType, HilbertValueType > | |
| CHilbertRTreeDescentHeuristic | This class chooses the best child of a node in a Hilbert R tree when inserting a new point |
| CHilbertRTreeSplit< splitOrder > | The splitting procedure for the Hilbert R tree |
| CHoeffdingCategoricalSplit< FitnessFunction > | This is the standard Hoeffding-bound categorical feature proposed in the paper below: |
| CHoeffdingNumericSplit< FitnessFunction, ObservationType > | The HoeffdingNumericSplit class implements the numeric feature splitting strategy alluded to by Domingos and Hulten in the following paper: |
| CHoeffdingTree< FitnessFunction, NumericSplitType, CategoricalSplitType > | The HoeffdingTree object represents all of the necessary information for a Hoeffding-bound-based decision tree |
| CHyperplaneBase< BoundT, ProjVectorT > | 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< TreeType > | |
| CIsSpillTree< tree::SpillTree< MetricType, StatisticType, MatType, HyperplaneType, SplitType > > | |
| CMeanSpaceSplit< MetricType, MatType > | |
| CMeanSplit< BoundType, MatType > | A binary space partitioning tree node is split into its left and right child |
| CMeanSplit< BoundType, MatType >::SplitInfo | An information about the partition |
| CMidpointSpaceSplit< MetricType, MatType > | |
| CMidpointSplit< BoundType, MatType > | A binary space partitioning tree node is split into its left and right child |
| CMidpointSplit< BoundType, MatType >::SplitInfo | A struct that contains an information about the split |
| CMinimalCoverageSweep< SplitPolicy > | The MinimalCoverageSweep class finds a partition along which we can split a node according to the coverage of two resulting nodes |
| CMinimalCoverageSweep< SplitPolicy >::SweepCost< TreeType > | A struct that provides the type of the sweep cost |
| CMinimalSplitsNumberSweep< SplitPolicy > | The MinimalSplitsNumberSweep class finds a partition along which we can split a node according to the number of required splits of the node |
| CMinimalSplitsNumberSweep< SplitPolicy >::SweepCost< typename > | A struct that provides the type of the sweep cost |
| CNoAuxiliaryInformation< TreeType > | |
| CNumericSplitInfo< ObservationType > | |
| COctree< MetricType, StatisticType, MatType > | |
| COctree< MetricType, StatisticType, MatType >::DualTreeTraverser< MetricType, StatisticType, MatType > | A dual-tree traverser; see dual_tree_traverser.hpp |
| COctree< MetricType, StatisticType, MatType >::SingleTreeTraverser< RuleType > | A single-tree traverser; see single_tree_traverser.hpp |
| CProjVector | ProjVector defines a general projection vector (not necessarily axis-parallel) |
| CQueueFrame< TreeType, TraversalInfoType > | |
| CRectangleTree< MetricType, StatisticType, MatType, SplitType, DescentType, AuxiliaryInformationType > | A rectangle type tree tree, such as an R-tree or X-tree |
| CRectangleTree< MetricType, StatisticType, MatType, SplitType, DescentType, AuxiliaryInformationType >::DualTreeTraverser< MetricType, StatisticType, MatType, SplitType, DescentType, AuxiliaryInformationType > | A dual tree traverser for rectangle type trees |
| CRectangleTree< MetricType, StatisticType, MatType, SplitType, DescentType, AuxiliaryInformationType >::SingleTreeTraverser< RuleType > | A single traverser for rectangle type trees |
| CRPlusPlusTreeAuxiliaryInformation< TreeType > | |
| 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< SplitPolicyType, SweepType > | 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< BoundType, MatType > | This class splits a node by a random hyperplane |
| CRPTreeMaxSplit< BoundType, MatType >::SplitInfo | An information about the partition |
| CRPTreeMeanSplit< BoundType, MatType > | This class splits a binary space tree |
| CRPTreeMeanSplit< BoundType, MatType >::SplitInfo | 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< MetricType, MatType > | |
| CSpillTree< MetricType, StatisticType, MatType, HyperplaneType, SplitType > | 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 |
| CSpillTree< MetricType, StatisticType, MatType, HyperplaneType, SplitType >::SpillDualTreeTraverser< MetricType, StatisticType, MatType, HyperplaneType, SplitType > | A generic dual-tree traverser for hybrid spill trees; see spill_dual_tree_traverser.hpp for implementation |
| CSpillTree< MetricType, StatisticType, MatType, HyperplaneType, SplitType >::SpillSingleTreeTraverser< MetricType, StatisticType, MatType, HyperplaneType, SplitType > | A generic single-tree traverser for hybrid spill trees; see spill_single_tree_traverser.hpp for implementation |
| CTraversalInfo< TreeType > | The TraversalInfo class holds traversal information which is used in dual-tree (and single-tree) traversals |
| CTreeTraits< TreeType > | 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< BoundType, MatType > | Split a node into two parts according to the median address of points contained in the node |
| CVantagePointSplit< BoundType, MatType, MaxNumSamples > | The class splits a binary space partitioning tree node according to the median distance to the vantage point |
| CVantagePointSplit< BoundType, MatType, MaxNumSamples >::SplitInfo | A struct that contains an information about the split |
| CXTreeAuxiliaryInformation< TreeType > | The XTreeAuxiliaryInformation class provides information specific to X trees for each node in a RectangleTree |
| CXTreeAuxiliaryInformation< TreeType >::SplitHistoryStruct | The X tree requires that the tree records it's "split history" |
| CXTreeSplit | A Rectangle Tree has new points inserted at the bottom |
| 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< N > | 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 |
| ▶CNeighborSearchStat< neighbor::NearestNeighborSort > | |
| CDualTreeKMeansStatistic | |
| ▶Ctemplate AuxiliarySplitInfo< ElemType > | |
| CDecisionTree< FitnessFunction, NumericSplitType, CategoricalSplitType, ElemType, NoRecursion > | This class implements a generic decision tree learner |
| CRangeType< double > | |
| CRangeType< ElemType > | |
| CRASearch< mlpack::tree::BinarySpaceTree > | |
| CRASearch< mlpack::tree::CoverTree > | |
| CRASearch< mlpack::tree::Octree > | |
| CRASearch< mlpack::tree::RectangleTree > | |
| CSDP< arma::sp_mat > | |
| ▶CT | |
| CPointerShim< T > | A shim for pointers |
