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mlpack ?.?.?

  • 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 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).

mlpack 3.2.2

  • 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 (#2092).

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

mlpack 3.2.1

  • 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

  • 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

  • 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

  • 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

  • Bump minimum CMake version to 3.3.2.

  • CMake fixes for Ninja generator by Marc Espie.

mlpack 3.0.3

  • 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 with documentation (#1460).

mlpack 3.0.2

  • 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

  • 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

  • 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

  • Compilation fix for some systems (#1082).

  • Fix PARAM_INT_OUT() (#1100).

mlpack 2.2.4

  • 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

  • Bug fix for --predictions_file in mlpack_decision_tree program.

mlpack 2.2.2

  • 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

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

mlpack 2.2.0

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

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

mlpack 1.0.11

  • 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

  • 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., does not exist).

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

mlpack 1.0.9

  • 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

  • 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

  • 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

  • Minor bugfix so that FastMKS gets built.

mlpack 1.0.5

  • 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

  • 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

  • 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

  • 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

  • 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

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