Mlpack

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).

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.

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.

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).

2013-06-13
* Minor bugfix so that FastMKS gets built.

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.