Man Pages in mlpack-doc

Section 3: Library calls

CMakeLists.txt.3

Src/mlpack/tests/cmakelists.txt -

IsVector.3

If value == true, then vectype is some sort of armadillo vector or subview.

IsVector_ arma_Col_ eT _ _.3

Isvector arma::col et -

IsVector_ arma_Row_ eT _ _.3

Isvector arma::row et -

IsVector_ arma_SpCol_ eT _ _.3

Isvector arma::spcol et -

IsVector_ arma_SpRow_ eT _ _.3

Isvector arma::sprow et -

IsVector_ arma_SpSubview_ eT _ _.3

Isvector arma::spsubview et -

IsVector_ arma_subview_col_ eT _ _.3

Isvector arma::subview_col et -

IsVector_ arma_subview_row_ eT _ _.3

Isvector arma::subview_row et -

RASearch.3

The rasearch class: this class provides a generic manner to perform rank-approximate search via random-sampling.

TREE_EXPLANATION.txt.3

Src/mlpack/core/tree/tree_explanation.txt -

TraversalInfo.3

The traversalinfo class holds traversal information which is used in dual-tree (and single-tree) traversals.

allow_empty_clusters.hpp.3

Src/mlpack/methods/kmeans/allow_empty_clusters.hpp -

amf.hpp.3

Src/mlpack/methods/amf/amf.hpp -

arma_traits.hpp.3

Src/mlpack/core/util/arma_traits.hpp -

aug_lagrangian.hpp.3

Src/mlpack/core/optimizers/aug_lagrangian/aug_lagrangian.hpp -

aug_lagrangian_function.hpp.3

Src/mlpack/core/optimizers/aug_lagrangian/aug_lagrangian_function.hpp -

aug_lagrangian_test_functions.hpp.3

Src/mlpack/core/optimizers/aug_lagrangian/aug_lagrangian_test_functions.hpp -

ballbound.hpp.3

Bounds that are useful for binary space partitioning trees.

binary_space_tree.hpp.3

Src/mlpack/core/tree/binary_space_tree.hpp -

bounds.hpp.3

Bounds that are useful for binary space partitioning trees.

bug.3

Bug list the counter variable is used in most cases to guarantee a unique global identifier for options declared using the param_*() macros. however, not all compilers have this support--most notably, gcc 4.3. in that case, the line macro is used as an attempt to get a unique global identifier, but collisions are still possible, and they produce bizarre error messages. see http://mlpack.org/trac/ticket/74 for more information. the counter variable is used in most cases to guarantee a unique global identifier for options declared using the param_*() macros. however, not all compilers have this support--most notably, gcc 4.3. in that case, the line macro is used as an attempt to get a unique global identifier, but collisions are still possible, and they produce bizarre error messages. see http://mlpack.org/trac/ticket/74 for more information. the counter variable is used in most cases to guarantee a unique global identifier for options declared using the param_*() macros. however, not all compilers have this support--most notably, gcc 4.3. in that case, the line macro is used as an attempt to get a unique global identifier, but collisions are still possible, and they produce bizarre error messages. see http://mlpack.org/trac/ticket/74 for more information. the counter variable is used in most cases to guarantee a unique global identifier for options declared using the param_*() macros. however, not all compilers have this support--most notably, gcc 4.3. in that case, the line macro is used as an attempt to get a unique global identifier, but collisions are still possible, and they produce bizarre error messages. see http://mlpack.org/trac/ticket/74 for more information. the counter variable is used in most cases to guarantee a unique global identifier for options declared using the param_*() macros. however, not all compilers have this support--most notably, gcc 4.3. in that case, the line macro is used as an attempt to get a unique global identifier, but collisions are still possible, and they produce bizarre error messages. see http://mlpack.org/trac/ticket/74 for more information. the counter variable is used in most cases to guarantee a unique global identifier for options declared using the param_*() macros. however, not all compilers have this support--most notably, gcc 4.3. in that case, the line macro is used as an attempt to get a unique global identifier, but collisions are still possible, and they produce bizarre error messages. see http://mlpack.org/trac/ticket/74 for more information. the counter variable is used in most cases to guarantee a unique global identifier for options declared using the param_*() macros. however, not all compilers have this support--most notably, gcc 4.3. in that case, the line macro is used as an attempt to get a unique global identifier, but collisions are still possible, and they produce bizarre error messages. see http://mlpack.org/trac/ticket/74 for more information. the counter variable is used in most cases to guarantee a unique global identifier for options declared using the param_*() macros. however, not all compilers have this support--most notably, gcc 4.3. in that case, the line macro is used as an attempt to get a unique global identifier, but collisions are still possible, and they produce bizarre error messages. see http://mlpack.org/trac/ticket/74 for more information. the counter variable is used in most cases to guarantee a unique global identifier for options declared using the param_*() macros. however, not all compilers have this support--most notably, gcc 4.3. in that case, the line macro is used as an attempt to get a unique global identifier, but collisions are still possible, and they produce bizarre error messages. see http://mlpack.org/trac/ticket/74 for more information. the counter variable is used in most cases to guarantee a unique global identifier for options declared using the param_*() macros. however, not all compilers have this support--most notably, gcc 4.3. in that case, the line macro is used as an attempt to get a unique global identifier, but collisions are still possible, and they produce bizarre error messages. see http://mlpack.org/trac/ticket/74 for more information. the counter variable is used in most cases to guarantee a unique global identifier for options declared using the param_*() macros. however, not all compilers have this support--most notably, gcc 4.3. in that case, the line macro is used as an attempt to get a unique global identifier, but collisions are still possible, and they produce bizarre error messages. see http://mlpack.org/trac/ticket/74 for more information. the counter variable is used in most cases to guarantee a unique global identifier for options declared using the param_*() macros. however, not all compilers have this support--most notably, gcc 4.3. in that case, the line macro is used as an attempt to get a unique global identifier, but collisions are still possible, and they produce bizarre error messages. see http://mlpack.org/trac/ticket/74 for more information.

build.3

Building mlpack

build.hpp.3

Doc/guide/build.hpp -

cf.hpp.3

Src/mlpack/methods/cf/cf.hpp -

clamp.hpp.3

Miscellaneous math clamping routines.

cli.hpp.3

Src/mlpack/core/util/cli.hpp -

cli_deleter.hpp.3

Src/mlpack/core/util/cli_deleter.hpp -

complete_incremental_termination.hpp.3

Src/mlpack/methods/amf/termination_policies/complete_incremental_termination.hpp -

core.hpp.3

Src/mlpack/core.hpp -

cosine_distance.hpp.3

Src/mlpack/core/kernels/cosine_distance.hpp -

cosine_tree.hpp.3

Src/mlpack/core/tree/cosine_tree/cosine_tree.hpp -

cover_tree.hpp.3

Src/mlpack/core/tree/cover_tree.hpp -

data_3d_ind.txt.3

Src/mlpack/tests/data/data_3d_ind.txt -

data_3d_mixed.txt.3

Src/mlpack/tests/data/data_3d_mixed.txt -

data_dependent_random_initializer.hpp.3

Src/mlpack/methods/sparse_coding/data_dependent_random_initializer.hpp -

decision_stump.hpp.3

Src/mlpack/methods/decision_stump/decision_stump.hpp -

det.txt.3

Tutorial for how to perform density estimation with density estimation trees (det).

dettutorial.3

Density estimation tree (det) tutorial

diagonal_constraint.hpp.3

Src/mlpack/methods/gmm/diagonal_constraint.hpp -

discrete_distribution.hpp.3

Src/mlpack/core/dists/discrete_distribution.hpp -

dt_utils.hpp.3

Src/mlpack/methods/det/dt_utils.hpp -

dtb.hpp.3

Src/mlpack/methods/emst/dtb.hpp -

dtb_rules.hpp.3

Src/mlpack/methods/emst/dtb_rules.hpp -

dtb_stat.hpp.3

Src/mlpack/methods/emst/dtb_stat.hpp -

dtree.hpp.3

Src/mlpack/methods/det/dtree.hpp -

dual_tree_traverser.hpp.3

Src/mlpack/core/tree/cover_tree/dual_tree_traverser.hpp -

edge_pair.hpp.3

Src/mlpack/methods/emst/edge_pair.hpp -

eigenvalue_ratio_constraint.hpp.3

Src/mlpack/methods/gmm/eigenvalue_ratio_constraint.hpp -

em_fit.hpp.3

Src/mlpack/methods/gmm/em_fit.hpp -

emst.txt.3

Tutorial for the euclidean minimum spanning tree algorithm.

emst_tutorial.3

Emst tutorial

epanechnikov_kernel.hpp.3

Src/mlpack/core/kernels/epanechnikov_kernel.hpp -

example_kernel.hpp.3

Src/mlpack/core/kernels/example_kernel.hpp -

example_tree.hpp.3

Src/mlpack/core/tree/example_tree.hpp -

exponential_schedule.hpp.3

Src/mlpack/core/optimizers/sa/exponential_schedule.hpp -

fastmks.hpp.3

Src/mlpack/methods/fastmks/fastmks.hpp -

fastmks.txt.3

Tutorial for how to use fastmks in mlpack.

fastmks_rules.hpp.3

Src/mlpack/methods/fastmks/fastmks_rules.hpp -

fastmks_stat.hpp.3

Src/mlpack/methods/fastmks/fastmks_stat.hpp -

first_point_is_root.hpp.3

Src/mlpack/core/tree/cover_tree/first_point_is_root.hpp -

fmkstutorial.3

Fast max-kernel search tutorial (fastmks)

furthest_neighbor_sort.hpp.3

Src/mlpack/methods/neighbor_search/sort_policies/furthest_neighbor_sort.hpp -

gaussian_distribution.hpp.3

Src/mlpack/core/dists/gaussian_distribution.hpp -

gaussian_kernel.hpp.3

Src/mlpack/core/kernels/gaussian_kernel.hpp -

gmm.hpp.3

Src/mlpack/methods/gmm/gmm.hpp -

hmm.hpp.3

Src/mlpack/methods/hmm/hmm.hpp -

hmm_util.hpp.3

Src/mlpack/methods/hmm/hmm_util.hpp -

hrectbound.hpp.3

Bounds that are useful for binary space partitioning trees.

hyperbolic_tangent_kernel.hpp.3

Src/mlpack/core/kernels/hyperbolic_tangent_kernel.hpp -

incomplete_incremental_termination.hpp.3

Src/mlpack/methods/amf/termination_policies/incomplete_incremental_termination.hpp -

iodoc.3

Mlpack input and output

iodoc.hpp.3

Doc/guide/iodoc.hpp -

ip_metric.hpp.3

Src/mlpack/core/metrics/ip_metric.hpp -

iris.txt.3

Src/mlpack/tests/data/iris.txt -

iris_labels.txt.3

Src/mlpack/tests/data/iris_labels.txt -

kernel_pca.hpp.3

Src/mlpack/methods/kernel_pca/kernel_pca.hpp -

kernel_traits.hpp.3

Src/mlpack/core/kernels/kernel_traits.hpp -

kmeans.hpp.3

Src/mlpack/methods/kmeans/kmeans.hpp -

kmeans.txt.3

Tutorial for how to use k-means in mlpack.

kmeans_selection.hpp.3

Src/mlpack/methods/nystroem_method/kmeans_selection.hpp -

kmtutorial.3

K-means tutorial (kmeans)

laplace_distribution.hpp.3

Src/mlpack/core/dists/laplace_distribution.hpp -

laplacian_kernel.hpp.3

Src/mlpack/core/kernels/laplacian_kernel.hpp -

lars.hpp.3

Src/mlpack/methods/lars/lars.hpp -

lbfgs.hpp.3

Src/mlpack/core/optimizers/lbfgs/lbfgs.hpp -

lcc.hpp.3

Src/mlpack/methods/local_coordinate_coding/lcc.hpp -

lin_alg.hpp.3

Src/mlpack/core/math/lin_alg.hpp -

linear_kernel.hpp.3

Src/mlpack/core/kernels/linear_kernel.hpp -

linear_regression.hpp.3

Src/mlpack/methods/linear_regression/linear_regression.hpp -

linear_regression.txt.3

Tutorial for how to use the linearregression class.

lmetric.hpp.3

Src/mlpack/core/metrics/lmetric.hpp -

load.hpp.3

Src/mlpack/core/data/load.hpp -

log.hpp.3

Src/mlpack/core/util/log.hpp -

logistic_regression.hpp.3

Src/mlpack/methods/logistic_regression/logistic_regression.hpp -

logistic_regression_function.hpp.3

Src/mlpack/methods/logistic_regression/logistic_regression_function.hpp -

lrsdp.hpp.3

Src/mlpack/core/optimizers/lrsdp/lrsdp.hpp -

lrsdp_function.hpp.3

Src/mlpack/core/optimizers/lrsdp/lrsdp_function.hpp -

lrtutorial.3

Linear/ridge regression tutorial (linear_regression)

lsh_search.hpp.3

Src/mlpack/methods/lsh/lsh_search.hpp -

mahalanobis_distance.hpp.3

Src/mlpack/core/metrics/mahalanobis_distance.hpp -

matrices.3

Matrices in mlpack

matrices.hpp.3

Doc/guide/matrices.hpp -

max_variance_new_cluster.hpp.3

Src/mlpack/methods/kmeans/max_variance_new_cluster.hpp -

mean_split.hpp.3

Src/mlpack/core/tree/binary_space_tree/mean_split.hpp -

mlpack.3

Linear algebra utility functions, generally performed on matrices or vectors.

mlpack_CLI.3

Parses the command line for parameters and holds user-specified parameters.

mlpack_Log.3

Provides a convenient way to give formatted output.

mlpack_ParamData.3

Aids in the extensibility of cli by focusing potential changes into one structure.

mlpack_Timer.3

The timer class provides a way for mlpack methods to be timed.

mlpack_Timers.3

Mlpack::timers -

mlpack_amf.3

Mlpack::amf -

mlpack_amf_AMF.3

This class implements amf (alternating matrix factorization) on the given matrix v.

mlpack_amf_CompleteIncrementalTermination.3

Mlpack::amf::completeincrementaltermination terminationpolicy -

mlpack_amf_IncompleteIncrementalTermination.3

Mlpack::amf::incompleteincrementaltermination terminationpolicy -

mlpack_amf_NMFALSUpdate.3

The alternating least square update rules of matrices w and h.

mlpack_amf_NMFMultiplicativeDistanceUpdate.3

The multiplicative distance update rules for matrices w and h.

mlpack_amf_NMFMultiplicativeDivergenceUpdate.3

Mlpack::amf::nmfmultiplicativedivergenceupdate -

mlpack_amf_RandomAcolInitialization.3

This class initializes the w matrix of the nmf algorithm by averaging p randomly chosen columns of v.

mlpack_amf_RandomInitialization.3

Mlpack::amf::randominitialization -

mlpack_amf_SVDBatchLearning.3

Mlpack::amf::svdbatchlearning -

mlpack_amf_SVDCompleteIncrementalLearning.3

Mlpack::amf::svdcompleteincrementallearning mattype -

mlpack_amf_SVDCompleteIncrementalLearning_ arma_sp_mat _.3

Mlpack::amf::svdcompleteincrementallearning arma::sp_mat -

mlpack_amf_SVDIncompleteIncrementalLearning.3

Mlpack::amf::svdincompleteincrementallearning -

mlpack_amf_SimpleResidueTermination.3

Mlpack::amf::simpleresiduetermination -

mlpack_amf_SimpleToleranceTermination.3

Mlpack::amf::simpletolerancetermination mattype -

mlpack_amf_ValidationRMSETermination.3

Mlpack::amf::validationrmsetermination mattype -

mlpack_bound.3

Mlpack::bound -

mlpack_bound_BallBound.3

Ball bound encloses a set of points at a specific distance (radius) from a specific point (center).

mlpack_bound_HRectBound.3

Hyper-rectangle bound for an l-metric.

mlpack_cf.3

Collaborative filtering.

mlpack_cf_CF.3

This class implements collaborative filtering (cf).

mlpack_data.3

Functions to load and save matrices.

mlpack_decision_stump.3

Mlpack::decision_stump -

mlpack_decision_stump_DecisionStump.3

This class implements a decision stump.

mlpack_det.3

Density estimation trees.

mlpack_det_DTree.3

A density estimation tree is similar to both a decision tree and a space partitioning tree (like a kd-tree).

mlpack_distribution.3

Probability distributions.

mlpack_distribution_DiscreteDistribution.3

A discrete distribution where the only observations are discrete observations.

mlpack_distribution_GaussianDistribution.3

A single multivariate gaussian distribution.

mlpack_distribution_LaplaceDistribution.3

The multivariate laplace distribution centered at 0 has pdf.

mlpack_emst.3

Euclidean minimum spanning trees.

mlpack_emst_DTBRules.3

Mlpack::emst::dtbrules metrictype, treetype -

mlpack_emst_DTBStat.3

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.

mlpack_emst_DualTreeBoruvka.3

Performs the mst calculation using the dual-tree boruvka algorithm, using any type of tree.

mlpack_emst_DualTreeBoruvka_SortEdgesHelper.3

For sorting the edge list after the computation.

mlpack_emst_EdgePair.3

An edge pair is simply two indices and a distance.

mlpack_emst_UnionFind.3

A union-find data structure.

mlpack_fastmks.3

Fast max-kernel search.

mlpack_fastmks_FastMKS.3

An implementation of fast exact max-kernel search.

mlpack_fastmks_FastMKSRules.3

The base case and pruning rules for fastmks (fast max-kernel search).

mlpack_fastmks_FastMKSStat.3

The statistic used in trees with fastmks.

mlpack_gmm.3

Gaussian mixture models.

mlpack_gmm_DiagonalConstraint.3

Force a covariance matrix to be diagonal.

mlpack_gmm_EMFit.3

This class contains methods which can fit a gmm to observations using the em algorithm.

mlpack_gmm_EigenvalueRatioConstraint.3

Given a vector of eigenvalue ratios, ensure that the covariance matrix always has those eigenvalue ratios.

mlpack_gmm_GMM.3

A gaussian mixture model (gmm).

mlpack_gmm_NoConstraint.3

This class enforces no constraint on the covariance matrix.

mlpack_gmm_PositiveDefiniteConstraint.3

Given a covariance matrix, force the matrix to be positive definite.

mlpack_hmm.3

Hidden markov models.

mlpack_hmm_HMM.3

A class that represents a hidden markov model with an arbitrary type of emission distribution.

mlpack_kernel.3

Kernel functions.

mlpack_kernel_CosineDistance.3

The cosine distance (or cosine similarity).

mlpack_kernel_EpanechnikovKernel.3

The epanechnikov kernel, defined as.

mlpack_kernel_ExampleKernel.3

An example kernel function.

mlpack_kernel_GaussianKernel.3

The standard gaussian kernel.

mlpack_kernel_HyperbolicTangentKernel.3

Hyperbolic tangent kernel.

mlpack_kernel_KMeansSelection.3

Mlpack::kernel::kmeansselection clusteringtype -

mlpack_kernel_KernelTraits.3

This is a template class that can provide information about various kernels.

mlpack_kernel_KernelTraits_ CosineDistance _.3

Kernel traits for the cosine distance.

mlpack_kernel_KernelTraits_ EpanechnikovKernel _.3

Kernel traits for the epanechnikov kernel.

mlpack_kernel_KernelTraits_ GaussianKernel _.3

Kernel traits for the gaussian kernel.

mlpack_kernel_KernelTraits_ LaplacianKernel _.3

Kernel traits of the laplacian kernel.

mlpack_kernel_KernelTraits_ SphericalKernel _.3

Kernel traits for the spherical kernel.

mlpack_kernel_KernelTraits_ TriangularKernel _.3

Kernel traits for the triangular kernel.

mlpack_kernel_LaplacianKernel.3

The standard laplacian kernel.

mlpack_kernel_LinearKernel.3

The simple linear kernel (dot product).

mlpack_kernel_NystroemMethod.3

Mlpack::kernel::nystroemmethod kerneltype, pointselectionpolicy -

mlpack_kernel_OrderedSelection.3

Mlpack::kernel::orderedselection -

mlpack_kernel_PSpectrumStringKernel.3

The p-spectrum string kernel.

mlpack_kernel_PolynomialKernel.3

The simple polynomial kernel.

mlpack_kernel_RandomSelection.3

Mlpack::kernel::randomselection -

mlpack_kernel_SphericalKernel.3

Mlpack::kernel::sphericalkernel -

mlpack_kernel_TriangularKernel.3

The trivially simple triangular kernel, defined by.

mlpack_kmeans.3

K-means clustering.

mlpack_kmeans_AllowEmptyClusters.3

Policy which allows k-means to create empty clusters without any error being reported.

mlpack_kmeans_KMeans.3

This class implements k-means clustering.

mlpack_kmeans_MaxVarianceNewCluster.3

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.

mlpack_kmeans_RandomPartition.3

A very simple partitioner which partitions the data randomly into the number of desired clusters.

mlpack_kmeans_RefinedStart.3

A refined approach for choosing initial points for k-means clustering.

mlpack_kpca.3

Mlpack::kpca -

mlpack_kpca_KernelPCA.3

This class performs kernel principal components analysis (kernel pca), for a given kernel.

mlpack_kpca_NaiveKernelRule.3

Mlpack::kpca::naivekernelrule kerneltype -

mlpack_kpca_NystroemKernelRule.3

Mlpack::kpca::nystroemkernelrule kerneltype, pointselectionpolicy -

mlpack_lcc.3

Mlpack::lcc -

mlpack_lcc_LocalCoordinateCoding.3

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.

mlpack_math.3

Miscellaneous math routines.

mlpack_math_Range.3

Simple real-valued range.

mlpack_metric.3

Mlpack::metric -

mlpack_metric_IPMetric.3

Mlpack::metric::ipmetric kerneltype -

mlpack_metric_LMetric.3

The l_p metric for arbitrary integer p, with an option to take the root.

mlpack_metric_MahalanobisDistance.3

The mahalanobis distance, which is essentially a stretched euclidean distance.

mlpack_mvu.3

Mlpack::mvu -

mlpack_mvu_MVU.3

The mvu class is meant to provide a good abstraction for users.

mlpack_naive_bayes.3

The naive bayes classifier.

mlpack_naive_bayes_NaiveBayesClassifier.3

The simple naive bayes classifier.

mlpack_nca.3

Neighborhood components analysis.

mlpack_nca_NCA.3

An implementation of neighborhood components analysis, both a linear dimensionality reduction technique and a distance learning technique.

mlpack_nca_SoftmaxErrorFunction.3

The 'softmax' stochastic neighbor assignment probability function.

mlpack_neighbor.3

Neighbor-search routines.

mlpack_neighbor_FurthestNeighborSort.3

This class implements the necessary methods for the sortpolicy template parameter of the neighborsearch class.

mlpack_neighbor_LSHSearch.3

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.

mlpack_neighbor_NearestNeighborSort.3

This class implements the necessary methods for the sortpolicy template parameter of the neighborsearch class.

mlpack_neighbor_NeighborSearch.3

The neighborsearch class is a template class for performing distance-based neighbor searches.

mlpack_neighbor_NeighborSearchRules.3

Mlpack::neighbor::neighborsearchrules sortpolicy, metrictype, treetype -

mlpack_neighbor_NeighborSearchStat.3

Extra data for each node in the tree.

mlpack_neighbor_NeighborSearchTraversalInfo.3

Traversal information for neighborsearch.

mlpack_neighbor_RASearchRules.3

Mlpack::neighbor::rasearchrules sortpolicy, metrictype, treetype -

mlpack_nn.3

Mlpack::nn -

mlpack_nn_SparseAutoencoder.3

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.

mlpack_nn_SparseAutoencoderFunction.3

This is a class for the sparse autoencoder objective function.

mlpack_optimization.3

Mlpack::optimization -

mlpack_optimization_AugLagrangian.3

The auglagrangian class implements the augmented lagrangian method of optimization.

mlpack_optimization_AugLagrangianFunction.3

This is a utility class used by auglagrangian, meant to wrap a lagrangianfunction into a function usable by a simple optimizer like l-bfgs.

mlpack_optimization_AugLagrangianTestFunction.3

This function is taken from 'practical mathematical optimization' (snyman), section 5.3.8 ('application of the augmented lagrangian method').

mlpack_optimization_ExponentialSchedule.3

The exponential cooling schedule cools the temperature t at every step according to the equation.

mlpack_optimization_GockenbachFunction.3

This function is taken from m.

mlpack_optimization_LRSDP.3

Lrsdp is the implementation of monteiro and burer's formulation of low-rank semidefinite programs (lr-sdp).

mlpack_optimization_LRSDPFunction.3

The objective function that lrsdp is trying to optimize.

mlpack_optimization_L_BFGS.3

The generic l-bfgs optimizer, which uses a back-tracking line search algorithm to minimize a function.

mlpack_optimization_LovaszThetaSDP.3

This function is the lovasz-theta semidefinite program, as implemented in the following paper:

mlpack_optimization_SA.3

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.

mlpack_optimization_SGD.3

Stochastic gradient descent is a technique for minimizing a function which can be expressed as a sum of other functions.

mlpack_optimization_test.3

Mlpack::optimization::test -

mlpack_optimization_test_RosenbrockWoodFunction.3

The generalized rosenbrock function in 4 dimensions with the wood function in four dimensions.

mlpack_optimization_test_SGDTestFunction.3

Very, very simple test function which is the composite of three other functions.

mlpack_pca.3

Mlpack::pca -

mlpack_pca_PCA.3

This class implements principal components analysis (pca).

mlpack_perceptron.3

Mlpack::perceptron -

mlpack_perceptron_Perceptron.3

This class implements a simple perceptron (i.e., a single layer neural network).

mlpack_perceptron_RandomInitialization.3

This class is used to initialize weights for the weightvectors matrix in a random manner.

mlpack_perceptron_SimpleWeightUpdate.3

Mlpack::perceptron::simpleweightupdate -

mlpack_perceptron_ZeroInitialization.3

This class is used to initialize the matrix weightvectors to zero.

mlpack_radical.3

Mlpack::radical -

mlpack_radical_Radical.3

An implementation of radical, an algorithm for independent component analysis (ica).

mlpack_range.3

Range-search routines.

mlpack_range_RangeSearch.3

The rangesearch class is a template class for performing range searches.

mlpack_range_RangeSearchRules.3

Mlpack::range::rangesearchrules metrictype, treetype -

mlpack_range_RangeSearchStat.3

Statistic class for rangesearch, to be set to the statistictype of the tree type that range search is being performed with.

mlpack_regression.3

Regression methods.

mlpack_regression_LARS.3

An implementation of lars, a stage-wise homotopy-based algorithm for l1-regularized linear regression (lasso) and l1+l2 regularized linear regression (elastic net).

mlpack_regression_LinearRegression.3

A simple linear regression algorithm using ordinary least squares.

mlpack_regression_LogisticRegression.3

Mlpack::regression::logisticregression optimizertype -

mlpack_regression_LogisticRegressionFunction.3

The log-likelihood function for the logistic regression objective function.

mlpack_sparse_coding.3

Mlpack::sparse_coding -

mlpack_sparse_coding_DataDependentRandomInitializer.3

A data-dependent random dictionary initializer for sparsecoding.

mlpack_sparse_coding_NothingInitializer.3

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().

mlpack_sparse_coding_RandomInitializer.3

A dictionaryinitializer for use with the sparsecoding class.

mlpack_sparse_coding_SparseCoding.3

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).

mlpack_svd.3

Mlpack::svd -

mlpack_svd_QUIC_SVD.3

Mlpack::svd::quic_svd -

mlpack_svd_RegularizedSVD.3

Mlpack::svd::regularizedsvd optimizertype -

mlpack_svd_RegularizedSVDFunction.3

Mlpack::svd::regularizedsvdfunction -

mlpack_tree.3

Trees and tree-building procedures.

mlpack_tree_BinarySpaceTree.3

A binary space partitioning tree, such as a kd-tree or a ball tree.

mlpack_tree_BinarySpaceTree_DualTreeTraverser.3

A dual-tree traverser for binary space trees; see dual_tree_traverser.hpp.

mlpack_tree_BinarySpaceTree_SingleTreeTraverser.3

A single-tree traverser for binary space trees; see single_tree_traverser.hpp for implementation.

mlpack_tree_CompareCosineNode.3

Mlpack::tree::comparecosinenode -

mlpack_tree_CosineTree.3

Mlpack::tree::cosinetree -

mlpack_tree_CoverTree.3

A cover tree is a tree specifically designed to speed up nearest-neighbor computation in high-dimensional spaces.

mlpack_tree_CoverTree_DualTreeTraverser.3

A dual-tree cover tree traverser; see dual_tree_traverser.hpp.

mlpack_tree_CoverTree_DualTreeTraverser_DualCoverTreeMapEntry.3

Struct used for traversal.

mlpack_tree_CoverTree_SingleTreeTraverser.3

A single-tree cover tree traverser; see single_tree_traverser.hpp for implementation.

mlpack_tree_EmptyStatistic.3

Empty statistic if you are not interested in storing statistics in your tree.

mlpack_tree_ExampleTree.3

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.

mlpack_tree_FirstPointIsRoot.3

This class is meant to be used as a choice for the policy class rootpointpolicy of the covertree class.

mlpack_tree_MRKDStatistic.3

Statistic for multi-resolution kd-trees.

mlpack_tree_MeanSplit.3

A binary space partitioning tree node is split into its left and right child.

mlpack_tree_TreeTraits.3

The treetraits class provides compile-time information on the characteristics of a given tree type.

mlpack_tree_TreeTraits_ BinarySpaceTree_ BoundType, StatisticType, MatType _ _.3

This is a specialization of the treetype class to the binaryspacetree tree type.

mlpack_tree_TreeTraits_ CoverTree_ MetricType, RootPointPolicy, StatisticType _ _.3

The specialization of the treetraits class for the covertree tree type.

mlpack_util.3

Mlpack::util -

mlpack_util_CLIDeleter.3

Extremely simple class whose only job is to delete the existing cli object at the end of execution.

mlpack_util_NullOutStream.3

Used for log::debug when not compiled with debugging symbols.

mlpack_util_Option.3

A static object whose constructor registers a parameter with the cli class.

mlpack_util_PrefixedOutStream.3

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.

mlpack_util_ProgramDoc.3

A static object whose constructor registers program documentation with the cli class.

mlpack_util_SaveRestoreUtility.3

Mlpack::util::saverestoreutility -

mrkd_statistic.hpp.3

Src/mlpack/core/tree/mrkd_statistic.hpp -

mvu.hpp.3

Src/mlpack/methods/mvu/mvu.hpp -

naive_bayes_classifier.hpp.3

Src/mlpack/methods/naive_bayes/naive_bayes_classifier.hpp -

naive_method.hpp.3

Src/mlpack/methods/kernel_pca/kernel_rules/naive_method.hpp -

nca.hpp.3

Src/mlpack/methods/nca/nca.hpp -

nca_softmax_error_function.hpp.3

Src/mlpack/methods/nca/nca_softmax_error_function.hpp -

nearest_neighbor_sort.hpp.3

Src/mlpack/methods/neighbor_search/sort_policies/nearest_neighbor_sort.hpp -

neighbor_search.hpp.3

Src/mlpack/methods/neighbor_search/neighbor_search.hpp -

neighbor_search.txt.3

Tutorial for how to use the neighborsearch class.

neighbor_search_rules.hpp.3

Src/mlpack/methods/neighbor_search/neighbor_search_rules.hpp -

neighbor_search_stat.hpp.3

Src/mlpack/methods/neighbor_search/neighbor_search_stat.hpp -

nmf_als.hpp.3

Src/mlpack/methods/amf/update_rules/nmf_als.hpp -

nmf_mult_dist.hpp.3

Src/mlpack/methods/amf/update_rules/nmf_mult_dist.hpp -

nmf_mult_div.hpp.3

Src/mlpack/methods/amf/update_rules/nmf_mult_div.hpp -

no_constraint.hpp.3

Src/mlpack/methods/gmm/no_constraint.hpp -

normalize_labels.hpp.3

Src/mlpack/core/data/normalize_labels.hpp -

nothing_initializer.hpp.3

Src/mlpack/methods/sparse_coding/nothing_initializer.hpp -

ns_traversal_info.hpp.3

Src/mlpack/methods/neighbor_search/ns_traversal_info.hpp -

nstutorial.3

Neighborsearch tutorial (k-nearest-neighbors)

nulloutstream.hpp.3

Src/mlpack/core/util/nulloutstream.hpp -

nystroem_method.hpp.3

Src/mlpack/methods/nystroem_method/nystroem_method.hpp -

old_boost_test_definitions.hpp.3

Src/mlpack/tests/old_boost_test_definitions.hpp -

option.hpp.3

Src/mlpack/core/util/option.hpp -

ordered_selection.hpp.3

Src/mlpack/methods/nystroem_method/ordered_selection.hpp -

pca.hpp.3

Src/mlpack/methods/pca/pca.hpp -

perceptron.hpp.3

Src/mlpack/methods/perceptron/perceptron.hpp -

phi.hpp.3

Src/mlpack/methods/gmm/phi.hpp -

polynomial_kernel.hpp.3

Src/mlpack/core/kernels/polynomial_kernel.hpp -

positive_definite_constraint.hpp.3

Src/mlpack/methods/gmm/positive_definite_constraint.hpp -

prefixedoutstream.hpp.3

Src/mlpack/core/util/prefixedoutstream.hpp -

prereqs.hpp.3

The core includes that mlpack expects; standard c++ includes and armadillo.

pspectrum_string_kernel.hpp.3

Src/mlpack/core/kernels/pspectrum_string_kernel.hpp -

quic_svd.hpp.3

Src/mlpack/methods/quic_svd/quic_svd.hpp -

ra_query_stat.hpp.3

Src/mlpack/methods/rann/ra_query_stat.hpp -

ra_search.hpp.3

Src/mlpack/methods/rann/ra_search.hpp -

ra_search_rules.hpp.3

Src/mlpack/methods/rann/ra_search_rules.hpp -

ra_typedef.hpp.3

Src/mlpack/methods/rann/ra_typedef.hpp -

radical.hpp.3

Src/mlpack/methods/radical/radical.hpp -

random.hpp.3

Miscellaneous math random-related routines.

random_acol_init.hpp.3

Src/mlpack/methods/amf/init_rules/random_acol_init.hpp -

random_init.hpp.3

Src/mlpack/methods/perceptron/initialization_methods/random_init.hpp -

random_initializer.hpp.3

Src/mlpack/methods/sparse_coding/random_initializer.hpp -

random_partition.hpp.3

Src/mlpack/methods/kmeans/random_partition.hpp -

random_selection.hpp.3

Src/mlpack/methods/nystroem_method/random_selection.hpp -

range.hpp.3

Definition of the range class, which represents a simple range with a lower and upper bound.

range_search.hpp.3

Src/mlpack/methods/range_search/range_search.hpp -

range_search.txt.3

Tutorial for how to use the rangesearch class.

range_search_rules.hpp.3

Src/mlpack/methods/range_search/range_search_rules.hpp -

range_search_stat.hpp.3

Src/mlpack/methods/range_search/range_search_stat.hpp -

rectangle_tree.hpp.3

Src/mlpack/core/tree/rectangle_tree.hpp -

refined_start.hpp.3

Src/mlpack/methods/kmeans/refined_start.hpp -

regularized_svd.hpp.3

Src/mlpack/methods/regularized_svd/regularized_svd.hpp -

regularized_svd_function.hpp.3

Src/mlpack/methods/regularized_svd/regularized_svd_function.hpp -

round.hpp.3

Src/mlpack/core/math/round.hpp -

rstutorial.3

Rangesearch tutorial (range_search)

sa.hpp.3

Src/mlpack/core/optimizers/sa/sa.hpp -

sample.3

Simple sample mlpack programs

sample.hpp.3

Doc/guide/sample.hpp -

save.hpp.3

Src/mlpack/core/data/save.hpp -

save_restore_utility.hpp.3

Src/mlpack/core/util/save_restore_utility.hpp -

sfinae_utility.hpp.3

Src/mlpack/core/util/sfinae_utility.hpp -

sgd.hpp.3

Src/mlpack/core/optimizers/sgd/sgd.hpp -

simple_residue_termination.hpp.3

Src/mlpack/methods/amf/termination_policies/simple_residue_termination.hpp -

simple_tolerance_termination.hpp.3

Src/mlpack/methods/amf/termination_policies/simple_tolerance_termination.hpp -

simple_weight_update.hpp.3

Src/mlpack/methods/perceptron/learning_policies/simple_weight_update.hpp -

single_tree_traverser.hpp.3

Src/mlpack/core/tree/cover_tree/single_tree_traverser.hpp -

sparse_autoencoder.hpp.3

Src/mlpack/methods/sparse_autoencoder/sparse_autoencoder.hpp -

sparse_autoencoder_function.hpp.3

Src/mlpack/methods/sparse_autoencoder/sparse_autoencoder_function.hpp -

sparse_coding.hpp.3

Src/mlpack/methods/sparse_coding/sparse_coding.hpp -

spherical_kernel.hpp.3

Src/mlpack/core/kernels/spherical_kernel.hpp -

statistic.hpp.3

Definition of the policy type for the statistic class.

string_util.hpp.3

Src/mlpack/core/util/string_util.hpp -

svd_batch_learning.hpp.3

Src/mlpack/methods/amf/update_rules/svd_batch_learning.hpp -

svd_complete_incremental_learning.hpp.3

Src/mlpack/methods/amf/update_rules/svd_complete_incremental_learning.hpp -

svd_incomplete_incremental_learning.hpp.3

Src/mlpack/methods/amf/update_rules/svd_incomplete_incremental_learning.hpp -

test_function.hpp.3

Src/mlpack/core/optimizers/sgd/test_function.hpp -

timer.3

Mlpack timers

timer.hpp.3

Doc/guide/timer.hpp -

timers.hpp.3

Src/mlpack/core/util/timers.hpp -

traits.hpp.3

Src/mlpack/core/tree/cover_tree/traits.hpp -

traversal_info.hpp.3

Src/mlpack/core/tree/traversal_info.hpp -

tree_traits.hpp.3

Src/mlpack/core/tree/tree_traits.hpp -

triangular_kernel.hpp.3

Src/mlpack/core/kernels/triangular_kernel.hpp -

tutorials.3

Tutorials

tutorials.txt.3

List of mlpack tutorials.

typedef.hpp.3

Src/mlpack/methods/neighbor_search/typedef.hpp -

union_find.hpp.3

Src/mlpack/methods/emst/union_find.hpp -

unmap.hpp.3

Src/mlpack/methods/neighbor_search/unmap.hpp -

validation_RMSE_termination.hpp.3

Src/mlpack/methods/amf/termination_policies/validation_rmse_termination.hpp -

verinfo.3

Mlpack version information

version.hpp.3

Doc/guide/version.hpp -

zero_init.hpp.3

Src/mlpack/methods/perceptron/initialization_methods/zero_init.hpp -