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### mlpack ?.?.?
###### ????-??-??
* Migrate from boost tests to Catch2 framework (#2523), (#2584).
* Bump minimum armadillo version from 8.400 to 9.800 (#3043), (#3048).
* Adding a copy constructor in the Convolution layer (#3067).
* Replace `boost::spirit` parser by a local efficient implementation (#2942).
* Disable correctly the autodownloader + fix tests stability (#3076).
* Replace `boost::any` with `core::v2::any` or `std::any` if available (#3006).
* Remove old non used Boost headers (#3005).
* Replace `boost::enable_if` with `std::enable_if` (#2998).
* Replace `boost::is_same` with `std::is_same` (#2993).
* Remove invalid option for emsmallen and STB (#2960).
* Check for armadillo dependencies before downloading armadillo (#2954).
* Disable the usage of autodownloader by default (#2953).
* Install dependencies downloaded with the autodownloader (#2952).
* Download older Boost if the compiler is old (#2940).
* Add support for embedded systems (#2531).
* Build mlpack executable statically if the library is statically linked (#2931).
* Fix cover tree loop bug on embedded arm systems (#2869).
* Fix a LAPACK bug in `FindArmadillo.cmake` (#2929).
* Add an autodownloader to get mlpack dependencies (#2927).
* Remove Coverage files and configurations from CMakeLists (#2866).
* Added `Multi Label Soft Margin Loss` loss function for neural networks
(#2345).
* Added Decision Tree Regressor (#2905). It can be used using the class
`mlpack::tree::DecisionTreeRegressor`. It is accessible only though C++.
* Added dict-style inspection of mlpack models in python bindings (#2868).
* Added Extra Trees Algorithm (#2883). Currently, it can be used using the
class `mlpack::tree::ExtraTrees`, but only through C++.
* Add Flatten T Swish activation function (`flatten-t-swish.hpp`)
* Added warm start feature to Random Forest (#2881); this feature is
accessible from mlpack's bindings to different languages.
* Added Pixel Shuffle layer (#2563).
* Add "check_input_matrices" option to python bindings that checks
for NaN and inf values in all the input matrices (#2787).
* Add Adjusted R squared functionality to R2Score::Evaluate (#2624).
* Disabled all the bindings by default in CMake (#2782).
* Added an implementation to Stratify Data (#2671).
* Add `BUILD_DOCS` CMake option to control whether Doxygen documentation is
built (default ON) (#2730).
* Add Triplet Margin Loss function (#2762).
* Add finalizers to Julia binding model types to fix memory handling (#2756).
* HMM: add functions to calculate likelihood for data stream with/without
pre-calculated emission probability (#2142).
* Replace Boost serialization library with Cereal (#2458).
* Add `PYTHON_INSTALL_PREFIX` CMake option to specify installation root for
Python bindings (#2797).
* Removed `boost::visitor` from model classes for `knn`, `kfn`, `cf`,
`range_search`, `krann`, and `kde` bindings (#2803).
* Add k-means++ initialization strategy (#2813).
* `NegativeLogLikelihood<>` now expects classes in the range `0` to
`numClasses - 1` (#2534).
* Add `Lambda1()`, `Lambda2()`, `UseCholesky()`, and `Tolerance()` members to
`LARS` so parameters for training can be modified (#2861).
* Remove unused `ElemType` template parameter from `DecisionTree` and
`RandomForest` (#2874).
* Fix Python binding build when the CMake variable `USE_OPENMP` is set to
`OFF` (#2884).
* The `mlpack_test` target is no longer built as part of `make all`. Use
`make mlpack_test` to build the tests.
* Fixes to `HoeffdingTree`: ensure that training still works when empty
constructor is used (#2964).
* Fix Julia model serialization bug (#2970).
* Fix `LoadCSV()` to use pre-populated `DatasetInfo` objects (#2980).
* Add `probabilities` option to softmax regression binding, to get class
probabilities for test points (#3001).
* Fix thread safety issues in mlpack bindings to other languages (#2995).
* Fix double-free of model pointers in R bindings (#3034).
* Fix Julia, Python, R, and Go handling of categorical data for
`decision_tree()` and `hoeffding_tree()` (#2971).
* Depend on `pkgbuild` for R bindings (#3081).
* Replaced Numpy deprecated code in Python bindings (#3126).
### mlpack 3.4.2
###### 2020-10-26
* Added Mean Absolute Percentage Error.
* Added Softmin activation function as layer in ann/layer.
* Fix spurious ARMA_64BIT_WORD compilation warnings on 32-bit systems (#2665).
### 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 `IOBuffer`s.
### 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
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