Mlpack

_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.

_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.

_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).

_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).

_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).

_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.