mlpack/HISTORY.md

mlpack ?.?.?

????-??-??

• Added Mean Absolute Percentage Error.

• Added Softmin activation function as layer in ann/layer.

mlpack 3.4.1

2020-09-07

• Fix incorrect parsing of required matrix/model parameters for command-line bindings (#2600).

• Add manual type specification support to data::Load() and data::Save() (#2084, #2135, #2602).

• Remove use of internal Armadillo functionality (#2596, #2601, #2602).

mlpack 3.4.0

2020-09-01

• Issue warnings when metrics produce NaNs in KFoldCV (#2595).

• Added bindings for R during Google Summer of Code (#2556).

• Added common striptype function for all bindings (#2556).

• Refactored common utility function of bindings to bindings/util (#2556).

• Renamed InformationGain to HoeffdingInformationGain in methods/hoeffding_trees/information_gain.hpp (#2556).

• Added macro for changing stream of printing and warnings/errors (#2556).

• Added Spatial Dropout layer (#2564).

• Force CMake to show error when it didn't find Python/modules (#2568).

• Refactor ProgramInfo() to separate out all the different information (#2558).

• Add bindings for one-hot encoding (#2325).

• Added Soft Actor-Critic to RL methods (#2487).

• Added Categorical DQN to q_networks (#2454).

• Added N-step DQN to q_networks (#2461).

• Add Silhoutte Score metric and Pairwise Distances (#2406).

• Add Go bindings for some missed models (#2460).

• Replace boost program_options dependency with CLI11 (#2459).

• Additional functionality for the ARFF loader (#2486); use case sensitive categories (#2516).

• Add bayesian_linear_regression binding for the command-line, Python, Julia, and Go. Also called "Bayesian Ridge", this is equivalent to a version of linear regression where the regularization parameter is automatically tuned (#2030).

• Fix defeatist search for spill tree traversals (#2566, #1269).

• Fix incremental training of logistic regression models (#2560).

• Change default configuration of BUILD_PYTHON_BINDINGS to OFF (#2575).

mlpack 3.3.2

2020-06-18

• Added Noisy DQN to q_networks (#2446).

• Add Go bindings (#1884).

• Added Dueling DQN to q_networks, Noisy linear layer to ann/layer and Empty loss to ann/loss_functions (#2414).

• Storing and adding accessor method for action in q_learning (#2413).

• Added accessor methods for ANN layers (#2321).

• Addition of Elliot activation function (#2268).

• Add adaptive max pooling and adaptive mean pooling layers (#2195).

• Add parameter to avoid shuffling of data in preprocess_split (#2293).

• Add MatType parameter to LSHSearch, allowing sparse matrices to be used for search (#2395).

• Documentation fixes to resolve Doxygen warnings and issues (#2400).

• Add Load and Save of Sparse Matrix (#2344).

• Add Intersection over Union (IoU) metric for bounding boxes (#2402).

• Add Non Maximal Supression (NMS) metric for bounding boxes (#2410).

• Fix no_intercept and probability computation for linear SVM bindings (#2419).

• Fix incorrect neighbors for k > 1 searches in approx_kfn binding, for the QDAFN algorithm (#2448).

• Fix serialization of kernels with state for FastMKS (#2452).

• Add RBF layer in ann module to make RBFN architecture (#2261).

mlpack 3.3.1

2020-04-29

• Minor Julia and Python documentation fixes (#2373).

• Updated terminal state and fixed bugs for Pendulum environment (#2354, #2369).

• Added EliSH activation function (#2323).

• Add L1 Loss function (#2203).

• Pass CMAKE_CXX_FLAGS (compilation options) correctly to Python build (#2367).

• Expose ensmallen Callbacks for sparseautoencoder (#2198).

• Bugfix for LARS class causing invalid read (#2374).

• Add serialization support from Julia; use mlpack.serialize() and mlpack.deserialize() to save and load from IOBuffers.

mlpack 3.3.0

2020-04-07

• Added Normal Distribution to ann/dists (#2382).

• Templated return type of Forward function of loss functions (#2339).

• Added R2 Score regression metric (#2323).

• Added poisson negative log likelihood loss function (#2196).

• Added huber loss function (#2199).

• Added mean squared logarithmic error loss function for neural networks (#2210).

• Added mean bias loss function for neural networks (#2210).

• The DecisionStump class has been marked deprecated; use the DecisionTree class with NoRecursion=true or use ID3DecisionStump instead (#2099).

• Added probabilities_file parameter to get the probabilities matrix of AdaBoost classifier (#2050).

• Fix STB header search paths (#2104).

• Add DISABLE_DOWNLOADS CMake configuration option (#2104).

• Add padding layer in TransposedConvolutionLayer (#2082).

• Fix pkgconfig generation on non-Linux systems (#2101).

• Use log-space to represent HMM initial state and transition probabilities (#2081).

• Add functions to access parameters of Convolution and AtrousConvolution layers (#1985).

• Add Compute Error function in lars regression and changing Train function to return computed error (#2139).

• Add Julia bindings (#1949). Build settings can be controlled with the BUILD_JULIA_BINDINGS=(ON/OFF) and JULIA_EXECUTABLE=/path/to/julia CMake parameters.

• CMake fix for finding STB include directory (#2145).

• Add bindings for loading and saving images (#2019); mlpack_image_converter from the command-line, mlpack.image_converter() from Python.

• Add normalization support for CF binding (#2136).

• Add Mish activation function (#2158).

• Update init_rules in AMF to allow users to merge two initialization rules (#2151).

• Add GELU activation function (#2183).

• Better error handling of eigendecompositions and Cholesky decompositions (#2088, #1840).

• Add LiSHT activation function (#2182).

• Add Valid and Same Padding for Transposed Convolution layer (#2163).

• Add CELU activation function (#2191)

• Add Log-Hyperbolic-Cosine Loss function (#2207).

• Change neural network types to avoid unnecessary use of rvalue references (#2259).

• Bump minimum Boost version to 1.58 (#2305).

• Refactor STB support so HAS_STB macro is not needed when compiling against mlpack (#2312).

• Add Hard Shrink Activation Function (#2186).

• Add Soft Shrink Activation Function (#2174).

• Add Hinge Embedding Loss Function (#2229).

• Add Cosine Embedding Loss Function (#2209).

• Add Margin Ranking Loss Function (#2264).

• Bugfix for incorrect parameter vector sizes in logistic regression and softmax regression (#2359).

mlpack 3.2.2

2019-11-26

• Add valid and same padding option in Convolution and Atrous Convolution layer (#1988).

• Add Model() to the FFN class to access individual layers (#2043).

• Update documentation for pip and conda installation packages (#2044).

• Add bindings for linear SVM (#1935); mlpack_linear_svm from the command-line, linear_svm() from Python.

• Add support to return the layer name as std::string (#1987).

• Speed and memory improvements for the Transposed Convolution layer (#1493).

• Fix Windows Python build configuration (#1885).

• Validate md5 of STB library after download (#2087).

• Add __version__ to __init__.py (#2092).

• Correctly handle RNN sequences that are shorter than the value of rho (#2102).

mlpack 3.2.1

2019-10-01

• Enforce CMake version check for ensmallen (#2032).

• Fix CMake check for Armadillo version (#2029).

• Better handling of when STB is not installed (#2033).

• Fix Naive Bayes classifier computations in high dimensions (#2022).

mlpack 3.2.0

2019-09-25

• Fix some potential infinity errors in Naive Bayes Classifier (#2022).

• Fix occasionally-failing RADICAL test (#1924).

• Fix gcc 9 OpenMP compilation issue (#1970).

• Added support for loading and saving of images (#1903).

• Add Multiple Pole Balancing Environment (#1901, #1951).

• Added functionality for scaling of data (#1876); see the command-line binding mlpack_preprocess_scale or Python binding preprocess_scale().

• Add new parameter maximum_depth to decision tree and random forest bindings (#1916).

• Fix prediction output of softmax regression when test set accuracy is calculated (#1922).

• Pendulum environment now checks for termination. All RL environments now have an option to terminate after a set number of time steps (no limit by default) (#1941).

• Add support for probabilistic KDE (kernel density estimation) error bounds when using the Gaussian kernel (#1934).

• Fix negative distances for cover tree computation (#1979).

• Fix cover tree building when all pairwise distances are 0 (#1986).

• Improve KDE pruning by reclaiming not used error tolerance (#1954, #1984).

• Optimizations for sparse matrix accesses in z-score normalization for CF (#1989).

• Add kmeans_max_iterations option to GMM training binding gmm_train_main.

• Bump minimum Armadillo version to 8.400.0 due to ensmallen dependency requirement (#2015).

mlpack 3.1.1

2019-05-26

• Fix random forest bug for numerical-only data (#1887).

• Significant speedups for random forest (#1887).

• Random forest now has minimum_gain_split and subspace_dim parameters (#1887).

• Decision tree parameter print_training_error deprecated in favor of print_training_accuracy.

• output option changed to predictions for adaboost and perceptron binding. Old options are now deprecated and will be preserved until mlpack 4.0.0 (#1882).

• Concatenated ReLU layer (#1843).

• Accelerate NormalizeLabels function using hashing instead of linear search (see src/mlpack/core/data/normalize_labels_impl.hpp) (#1780).

• Add ConfusionMatrix() function for checking performance of classifiers (#1798).

• Install ensmallen headers when it is downloaded during build (#1900).

mlpack 3.1.0

2019-04-25

• Add DiagonalGaussianDistribution and DiagonalGMM classes to speed up the diagonal covariance computation and deprecate DiagonalConstraint (#1666).

• Add kernel density estimation (KDE) implementation with bindings to other languages (#1301).

• Where relevant, all models with a Train() method now return a double value representing the goodness of fit (i.e. final objective value, error, etc.) (#1678).

• Add implementation for linear support vector machine (see src/mlpack/methods/linear_svm).

• Change DBSCAN to use PointSelectionPolicy and add OrderedPointSelection (#1625).

• Residual block support (#1594).

• Bidirectional RNN (#1626).

• Dice loss layer (#1674, #1714) and hard sigmoid layer (#1776).

• output option changed to predictions and output_probabilities to probabilities for Naive Bayes binding (mlpack_nbc/nbc()). Old options are now deprecated and will be preserved until mlpack 4.0.0 (#1616).

• Add support for Diagonal GMMs to HMM code (#1658, #1666). This can provide large speedup when a diagonal GMM is acceptable as an emission probability distribution.

• Python binding improvements: check parameter type (#1717), avoid copying Pandas dataframes (#1711), handle Pandas Series objects (#1700).

mlpack 3.0.4

2018-11-13

• Bump minimum CMake version to 3.3.2.

• CMake fixes for Ninja generator by Marc Espie.

mlpack 3.0.3

2018-07-27

• Fix Visual Studio compilation issue (#1443).

• Allow running local_coordinate_coding binding with no initial_dictionary parameter when input_model is not specified (#1457).

• Make use of OpenMP optional via the CMake 'USE_OPENMP' configuration variable (#1474).

• Accelerate FNN training by 20-30% by avoiding redundant calculations (#1467).

• Fix math::RandomSeed() usage in tests (#1462, #1440).

• Generate better Python setup.py with documentation (#1460).

mlpack 3.0.2

2018-06-08

• Documentation generation fixes for Python bindings (#1421).

• Fix build error for man pages if command-line bindings are not being built (#1424).

• Add 'shuffle' parameter and Shuffle() method to KFoldCV (#1412). This will shuffle the data when the object is constructed, or when Shuffle() is called.

• Added neural network layers: AtrousConvolution (#1390), Embedding (#1401), and LayerNorm (layer normalization) (#1389).

• Add Pendulum environment for reinforcement learning (#1388) and update Mountain Car environment (#1394).

mlpack 3.0.1

2018-05-10

• Fix intermittently failing tests (#1387).

• Add big-batch SGD (BBSGD) optimizer in src/mlpack/core/optimizers/bigbatch_sgd/ (#1131).

• Fix simple compiler warnings (#1380, #1373).

• Simplify NeighborSearch constructor and Train() overloads (#1378).

• Add warning for OpenMP setting differences (#1358/#1382). When mlpack is compiled with OpenMP but another application is not (or vice versa), a compilation warning will now be issued.

• Restructured loss functions in src/mlpack/methods/ann/ (#1365).

• Add environments for reinforcement learning tests (#1368, #1370, #1329).

• Allow single outputs for multiple timestep inputs for recurrent neural networks (#1348).

• Add He and LeCun normal initializations for neural networks (#1342). Neural networks: add He and LeCun normal initializations (#1342), add FReLU and SELU activation functions (#1346, #1341), add alpha-dropout (#1349).

mlpack 3.0.0

2018-03-30

• Speed and memory improvements for DBSCAN. --single_mode can now be used for situations where previously RAM usage was too high.

• Bump minimum required version of Armadillo to 6.500.0.

• Add automatically generated Python bindings. These have the same interface as the command-line programs.

• Add deep learning infrastructure in src/mlpack/methods/ann/.

• Add reinforcement learning infrastructure in src/mlpack/methods/reinforcement_learning/.

• Add optimizers: AdaGrad, CMAES, CNE, FrankeWolfe, GradientDescent, GridSearch, IQN, Katyusha, LineSearch, ParallelSGD, SARAH, SCD, SGDR, SMORMS3, SPALeRA, SVRG.

• Add hyperparameter tuning infrastructure and cross-validation infrastructure in src/mlpack/core/cv/ and src/mlpack/core/hpt/.

• Fix bug in mean shift.

• Add random forests (see src/mlpack/methods/random_forest).

• Numerous other bugfixes and testing improvements.

• Add randomized Krylov SVD and Block Krylov SVD.

mlpack 2.2.5

2017-08-25

• Compilation fix for some systems (#1082).

• Fix PARAM_INT_OUT() (#1100).

mlpack 2.2.4

2017-07-18

• Speed and memory improvements for DBSCAN. --single_mode can now be used for situations where previously RAM usage was too high.

• Fix bug in CF causing incorrect recommendations.

mlpack 2.2.3

2017-05-24

• Bug fix for --predictions_file in mlpack_decision_tree program.

mlpack 2.2.2

2017-05-04

• Install backwards-compatibility mlpack_allknn and mlpack_allkfn programs; note they are deprecated and will be removed in mlpack 3.0.0 (#992).

• Fix RStarTree bug that surfaced on OS X only (#964).

• Small fixes for MiniBatchSGD and SGD and tests.

mlpack 2.2.1

2017-04-13

• Compilation fix for mlpack_nca and mlpack_test on older Armadillo versions (#984).

mlpack 2.2.0

2017-03-21

• Bugfix for mlpack_knn program (#816).

• Add decision tree implementation in methods/decision_tree/. This is very similar to a C4.5 tree learner.

• Add DBSCAN implementation in methods/dbscan/.

• Add support for multidimensional discrete distributions (#810, #830).

• Better output for Log::Debug/Log::Info/Log::Warn/Log::Fatal for Armadillo objects (#895, #928).

• Refactor categorical CSV loading with boost::spirit for faster loading (#681).

mlpack 2.1.1

2016-12-22

• HMMs now use random initialization; this should fix some convergence issues (#828).

• HMMs now initialize emissions according to the distribution of observations (#833).

• Minor fix for formatted output (#814).

• Fix DecisionStump to properly work with any input type.

mlpack 2.1.0

2016-10-31

• Fixed CoverTree to properly handle single-point datasets.

• Fixed a bug in CosineTree (and thus QUIC-SVD) that caused split failures for some datasets (#717).

• Added mlpack_preprocess_describe program, which can be used to print statistics on a given dataset (#742).

• Fix prioritized recursion for k-furthest-neighbor search (mlpack_kfn and the KFN class), leading to orders-of-magnitude speedups in some cases.

• Bump minimum required version of Armadillo to 4.200.0.

• Added simple Gradient Descent optimizer, found in src/mlpack/core/optimizers/gradient_descent/ (#792).

• Added approximate furthest neighbor search algorithms QDAFN and DrusillaSelect in src/mlpack/methods/approx_kfn/, with command-line program mlpack_approx_kfn.

mlpack 2.0.3

2016-07-21

• Added multiprobe LSH (#691). The parameter 'T' to LSHSearch::Search() can now be used to control the number of extra bins that are probed, as can the -T (--num_probes) option to mlpack_lsh.

• Added the Hilbert R tree to src/mlpack/core/tree/rectangle_tree/ (#664). It can be used as the typedef HilbertRTree, and it is now an option in the mlpack_knn, mlpack_kfn, mlpack_range_search, and mlpack_krann command-line programs.

• Added the mlpack_preprocess_split and mlpack_preprocess_binarize programs, which can be used for preprocessing code (#650, #666).

• Added OpenMP support to LSHSearch and mlpack_lsh (#700).

mlpack 2.0.2

2016-06-20

• Added the function LSHSearch::Projections(), which returns an arma::cube with each projection table in a slice (#663). Instead of Projection(i), you should now use Projections().slice(i).

• A new constructor has been added to LSHSearch that creates objects using projection tables provided in an arma::cube (#663).

• Handle zero-variance dimensions in DET (#515).

• Add MiniBatchSGD optimizer (src/mlpack/core/optimizers/minibatch_sgd/) and allow its use in mlpack_logistic_regression and mlpack_nca programs.

• Add better backtrace support from Grzegorz Krajewski for Log::Fatal messages when compiled with debugging and profiling symbols. This requires libbfd and libdl to be present during compilation.

• CosineTree test fix from Mikhail Lozhnikov (#358).

• Fixed HMM initial state estimation (#600).

• Changed versioning macros __MLPACK_VERSION_MAJOR, __MLPACK_VERSION_MINOR, and __MLPACK_VERSION_PATCH to MLPACK_VERSION_MAJOR, MLPACK_VERSION_MINOR, and MLPACK_VERSION_PATCH. The old names will remain in place until mlpack 3.0.0.

• Renamed mlpack_allknn, mlpack_allkfn, and mlpack_allkrann to mlpack_knn, mlpack_kfn, and mlpack_krann. The mlpack_allknn, mlpack_allkfn, and mlpack_allkrann programs will remain as copies until mlpack 3.0.0.

• Add --random_initialization option to mlpack_hmm_train, for use when no labels are provided.

• Add --kill_empty_clusters option to mlpack_kmeans and KillEmptyClusters policy for the KMeans class (#595, #596).

mlpack 2.0.1

2016-02-04

• Fix CMake to properly detect when MKL is being used with Armadillo.

• Minor parameter handling fixes to mlpack_logistic_regression (#504, #505).

• Properly install arma_config.hpp.

• Memory handling fixes for Hoeffding tree code.

• Add functions that allow changing training-time parameters to HoeffdingTree class.

• Fix infinite loop in sparse coding test.

• Documentation spelling fixes (#501).

• Properly handle covariances for Gaussians with large condition number (#496), preventing GMMs from filling with NaNs during training (and also HMMs that use GMMs).

• CMake fixes for finding LAPACK and BLAS as Armadillo dependencies when ATLAS is used.

• CMake fix for projects using mlpack's CMake configuration from elsewhere (#512).

mlpack 2.0.0

2015-12-24

• Removed overclustering support from k-means because it is not well-tested, may be buggy, and is (I think) unused. If this was support you were using, open a bug or get in touch with us; it would not be hard for us to reimplement it.

• Refactored KMeans to allow different types of Lloyd iterations.

• Added implementations of k-means: Elkan's algorithm, Hamerly's algorithm, Pelleg-Moore's algorithm, and the DTNN (dual-tree nearest neighbor) algorithm.

• Significant acceleration of LRSDP via the use of accu(a % b) instead of trace(a * b).

• Added MatrixCompletion class (matrix_completion), which performs nuclear norm minimization to fill unknown values of an input matrix.

• No more dependence on Boost.Random; now we use C++11 STL random support.

• Add softmax regression, contributed by Siddharth Agrawal and QiaoAn Chen.

• Changed NeighborSearch, RangeSearch, FastMKS, LSH, and RASearch API; these classes now take the query sets in the Search() method, instead of in the constructor.

• Use OpenMP, if available. For now OpenMP support is only available in the DET training code.

• Add support for predicting new test point values to LARS and the command-line 'lars' program.

• Add serialization support for Perceptron and LogisticRegression.

• Refactor SoftmaxRegression to predict into an arma::Row<size_t> object, and add a softmax_regression program.

• Refactor LSH to allow loading and saving of models.

• ToString() is removed entirely (#487).

• Add --input_model_file and --output_model_file options to appropriate machine learning algorithms.

• Rename all executables to start with an "mlpack" prefix (#229).

• Add HoeffdingTree and mlpack_hoeffding_tree, an implementation of the streaming decision tree methodology from Domingos and Hulten in 2000.

mlpack 1.0.12

2015-01-07

• Switch to 3-clause BSD license (from LGPL).

mlpack 1.0.11

2014-12-11

• Proper handling of dimension calculation in PCA.

• Load parameter vectors properly for LinearRegression models.

• Linker fixes for AugLagrangian specializations under Visual Studio.

• Add support for observation weights to LinearRegression.

• MahalanobisDistance<> now takes the root of the distance by default and therefore satisfies the triangle inequality (TakeRoot now defaults to true).

• Better handling of optional Armadillo HDF5 dependency.

• Fixes for numerous intermittent test failures.

• math::RandomSeed() now sets the random seed for recent (>=3.930) Armadillo versions.

• Handle Newton method convergence better for SparseCoding::OptimizeDictionary() and make maximum iterations a parameter.

• Known bug: CosineTree construction may fail in some cases on i386 systems (#358).

mlpack 1.0.10

2014-08-29

• Bugfix for NeighborSearch regression which caused very slow allknn/allkfn. Speeds are now restored to approximately 1.0.8 speeds, with significant improvement for the cover tree (#347).

• Detect dependencies correctly when ARMA_USE_WRAPPER is not being defined (i.e., libarmadillo.so does not exist).

• Bugfix for compilation under Visual Studio (#348).

mlpack 1.0.9

2014-07-28

• GMM initialization is now safer and provides a working GMM when constructed with only the dimensionality and number of Gaussians (#301).

• Check for division by 0 in Forward-Backward Algorithm in HMMs (#301).

• Fix MaxVarianceNewCluster (used when re-initializing clusters for k-means) (#301).

• Fixed implementation of Viterbi algorithm in HMM::Predict() (#303).

• Significant speedups for dual-tree algorithms using the cover tree (#235, #314) including a faster implementation of FastMKS.

• Fix for LRSDP optimizer so that it compiles and can be used (#312).

• CF (collaborative filtering) now expects users and items to be zero-indexed, not one-indexed (#311).

• CF::GetRecommendations() API change: now requires the number of recommendations as the first parameter. The number of users in the local neighborhood should be specified with CF::NumUsersForSimilarity().

• Removed incorrect PeriodicHRectBound (#58).

• Refactor LRSDP into LRSDP class and standalone function to be optimized (#305).

• Fix for centering in kernel PCA (#337).

• Added simulated annealing (SA) optimizer, contributed by Zhihao Lou.

• HMMs now support initial state probabilities; these can be set in the constructor, trained, or set manually with HMM::Initial() (#302).

• Added Nyström method for kernel matrix approximation by Marcus Edel.

• Kernel PCA now supports using Nyström method for approximation.

• Ball trees now work with dual-tree algorithms, via the BallBound<> bound structure (#307); fixed by Yash Vadalia.

• The NMF class is now AMF<>, and supports far more types of factorizations, by Sumedh Ghaisas.

• A QUIC-SVD implementation has returned, written by Siddharth Agrawal and based on older code from Mudit Gupta.

• Added perceptron and decision stump by Udit Saxena (these are weak learners for an eventual AdaBoost class).

• Sparse autoencoder added by Siddharth Agrawal.

mlpack 1.0.8

2014-01-06

• Memory leak in NeighborSearch index-mapping code fixed (#298).

• GMMs can be trained using the existing model as a starting point by specifying an additional boolean parameter to GMM::Estimate() (#296).

• Logistic regression implementation added in methods/logistic_regression (see also #293).

• L-BFGS optimizer now returns its function via Function().

• Version information is now obtainable via mlpack::util::GetVersion() or the __MLPACK_VERSION_MAJOR, __MLPACK_VERSION_MINOR, and __MLPACK_VERSION_PATCH macros (#297).

• Fix typos in allkfn and allkrann output.

mlpack 1.0.7

2013-10-04

• Cover tree support for range search (range_search), rank-approximate nearest neighbors (allkrann), minimum spanning tree calculation (emst), and FastMKS (fastmks).

• Dual-tree FastMKS implementation added and tested.

• Added collaborative filtering package (cf) that can provide recommendations when given users and items.

• Fix for correctness of Kernel PCA (kernel_pca) (#270).

• Speedups for PCA and Kernel PCA (#198).

• Fix for correctness of Neighborhood Components Analysis (NCA) (#279).

• Minor speedups for dual-tree algorithms.

• Fix for Naive Bayes Classifier (nbc) (#269).

• Added a ridge regression option to LinearRegression (linear_regression) (#286).

• Gaussian Mixture Models (gmm::GMM<>) now support arbitrary covariance matrix constraints (#283).

• MVU (mvu) removed because it is known to not work (#183).

• Minor updates and fixes for kernels (in mlpack::kernel).

mlpack 1.0.6

2013-06-13

• Minor bugfix so that FastMKS gets built.

mlpack 1.0.5

2013-05-01

• Speedups of cover tree traversers (#235).

• Addition of rank-approximate nearest neighbors (RANN), found in src/mlpack/methods/rann/.

• Addition of fast exact max-kernel search (FastMKS), found in src/mlpack/methods/fastmks/.

• Fix for EM covariance estimation; this should improve GMM training time.

• More parameters for GMM estimation.

• Force GMM and GaussianDistribution covariance matrices to be positive definite, so that training converges much more often.

• Add parameter for the tolerance of the Baum-Welch algorithm for HMM training.

• Fix for compilation with clang compiler.

• Fix for k-furthest-neighbor-search.

mlpack 1.0.4

2013-02-08

• Force minimum Armadillo version to 2.4.2.

• Better output of class types to streams; a class with a ToString() method implemented can be sent to a stream with operator<<.

• Change return type of GMM::Estimate() to double (#257).

• Style fixes for k-means and RADICAL.

• Handle size_t support correctly with Armadillo 3.6.2 (#258).

• Add locality-sensitive hashing (LSH), found in src/mlpack/methods/lsh/.

• Better tests for SGD (stochastic gradient descent) and NCA (neighborhood components analysis).

mlpack 1.0.3

2012-09-16

• Remove internal sparse matrix support because Armadillo 3.4.0 now includes it. When using Armadillo versions older than 3.4.0, sparse matrix support is not available.

• NCA (neighborhood components analysis) now support an arbitrary optimizer (#245), including stochastic gradient descent (#249).

mlpack 1.0.2

2012-08-15

• Added density estimation trees, found in src/mlpack/methods/det/.

• Added non-negative matrix factorization, found in src/mlpack/methods/nmf/.

• Added experimental cover tree implementation, found in src/mlpack/core/tree/cover_tree/ (#157).

• Better reporting of boost::program_options errors (#225).

• Fix for timers on Windows (#212, #211).

• Fix for allknn and allkfn output (#204).

• Sparse coding dictionary initialization is now a template parameter (#220).

mlpack 1.0.1

2012-03-03

• Added kernel principal components analysis (kernel PCA), found in src/mlpack/methods/kernel_pca/ (#74).

• Fix for Lovasz-Theta AugLagrangian tests (#182).

• Fixes for allknn output (#185, #186).

• Added range search executable (#192).

• Adapted citations in documentation to BibTeX; no citations in -h output (#195).

• Stop use of 'const char*' and prefer 'std::string' (#176).

• Support seeds for random numbers (#177).

mlpack 1.0.0

2011-12-17

• Initial release. See any resolved tickets numbered less than #196 or execute this query: http://www.mlpack.org/trac/query?status=closed&milestone=mlpack+1.0.0