december 11th, 2014 -- 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 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 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 (376).

- 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 (#365).
- Detect dependencies correctly when ARMA_USE_WRAPPER is not defined (i.e., libarmadillo.so does not exist).
- Bugfix for compilation under Visual Studio (#366).

- GMM initialization is now safer and provides a working GMM when constructed with only the dimensionality and number of Gaussians (#314).
- Check for division by 0 in Forward-Backward algorithm in HMMs (#314).
- Fixed implementation of Viterbi algorithm in HMM::Predict() (#316)
- Significant speedups for dual-tree algorithms using the cover tree (#243, #329) including a faster implementation of FastMKS.
- CF (collaborative filtering) now expects users and items to be zero-indexed, not one-indexed (#324).
- 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 (#30).
- Refactor LRSDP into LRSDP class and standalone function to be optimized (#318).
- Fix for centering in kernel PCA (#355).
- 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() (#315).
- Added Nyström method for kernel matrix approximation by Marcus Edel.
- Kernel PCA now supports using the Nyström method for approximation.
- Ball trees now work with dual-tree algorithms, via the BallBound<> bound structure (#320); 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.

- Memory leak in NeighborSearch index-mapping code fixed.
- GMMs can be trained using the existing model as a starting point by specifying an additional boolean parameter to GMM::Estimate().
- Logistic regression implementation added in methods/logistic_regression.
- Version information is now obtainable via mlpack::util::GetVersion() or the __MLPACK_VERSION_MAJOR, __MLPACK_VERSION_MINOR, and __MLPACK_VERSION_PATCH macros.
- Fix typos in allkfn and allkrann output.

- 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) (#280).
- Speedups for PCA and Kernel PCA (#204).
- Fix for correctness of Neighborhood Components Analysis (NCA) (#289).
- Minor speedups for dual-tree algorithms.
- Fix for Naive Bayes Classifier (nbc) (#279).
- Added a ridge regression option to LinearRegression (linear_regression) (#298).
- Gaussian Mixture Models (gmm::GMM<>) now support arbitrary covariance matrix constraints (#294).
- MVU (mvu) removed because it is known to not work (#189).
- Minor updates and fixes for kernels (in mlpack::kernel).

- Minor bugfix so that FastMKS gets built.

- Speedups of cover tree traversers (#243).
- 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.

- 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<<. See #164.
- Change return type of GMM::Estimate() to double (#266).
- Style fixes for k-means and RADICAL.
- Handle size_t support correctly with Armadillo 3.6.2 (#267).
- Add locality-sensitive hashing (LSH), found in src/mlpack/methods/lsh/.
- Better tests for SGD (stochastic gradient descent) and NCA (neighborhood components analysis).

- 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 (#254), including stochastic gradient descent (#258).

- 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/ (#156)
- Better reporting of boost::program_options errors (#231).
- Fix for timers on Windows (#218, #217).
- Fix for allknn and allkfn output (#210).
- Sparse coding dictionary initialization is now a template parameter (#226).

- Added kernel principal components analysis (kernel PCA), found in src/mlpack/methods/kernel_pca/ (#47).
- Fix for Lovasz-Theta AugLagrangian tests (#188).
- Fixes for allknn output (#191, #192).
- Added range search executable (#198).
- Adapted citations in documentation to BiBTeX; no citations in -h output (#201).
- Stop use of 'const char*' and prefer 'std::string' (#183).
- Support seeds for random numbers (#182).

- Initial release. See any resolved tickets numbered less than #196 or execute this query.