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Fix CMake to properly detect when MKL is being used with Armadillo.
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Minor parameter handling fixes to mlpack_logistic_regression (#504, #505).
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Properly install arma_config.hpp.
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Memory handling fixes for Hoeffding tree code.
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Add functions that allow changing training-time parameters to HoeffdingTree class.
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Fix infinite loop in sparse coding test.
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Documentation spelling fixes (#501).
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Properly handle covariances for Gaussians with large condition number (#496), preventing GMMs from filling with NaNs during training (and also HMMs that use GMMs).
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CMake fixes for finding LAPACK and BLAS as Armadillo dependencies when ATLAS is used.
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CMake fix for projects using mlpack's CMake configuration from elsewhere (#512).
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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.
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Refactored KMeans to allow different types of Lloyd iterations.
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Added implementations of k-means: Elkan's algorithm, Hamerly's algorithm, Pelleg-Moore's algorithm, and the DTNN (dual-tree nearest neighbor) algorithm.
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Significant acceleration of LRSDP via the use of accu(a % b) instead of trace(a * b).
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Added MatrixCompletion class (matrix_completion), which performs nuclear norm minimization to fill unknown values of an input matrix.
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No more dependence on Boost.Random; now we use C++11 STL random support.
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Add softmax regression, contributed by Siddharth Agrawal and QiaoAn Chen.
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Changed NeighborSearch, RangeSearch, FastMKS, LSH, and RASearch API; these classes now take the query sets in the Search() method, instead of in the constructor.
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Use OpenMP, if available. For now OpenMP support is only available in the DET training code.
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Add support for predicting new test point values to LARS and the command-line 'lars' program.
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Add serialization support for Perceptron and LogisticRegression.
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Refactor SoftmaxRegression to predict into an arma::Row<size_t> object, and add a softmax_regression program.
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Refactor LSH to allow loading and saving of models.
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ToString() is removed entirely (#487).
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Add --input_model_file and --output_model_file options to appropriate machine learning algorithms.
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Rename all executables to start with an "mlpack" prefix (#229).
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Add HoeffdingTree and mlpack_hoeffding_tree, an implementation of the streaming decision tree methodology from Domingos and Hulten in 2000.
- Switch to 3-clause BSD license (from LGPL).
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Proper handling of dimension calculation in PCA.
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Load parameter vectors properly for LinearRegression models.
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Linker fixes for AugLagrangian specializations under Visual Studio.
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Add support for observation weights to LinearRegression.
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MahalanobisDistance<> now takes root of the distance by default and therefore satisfies the triangle inequality (TakeRoot now defaults to true).
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Better handling of optional Armadillo HDF5 dependency.
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Fixes for numerous intermittent test failures.
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math::RandomSeed() now sets the random seed for recent (>=3.930) Armadillo versions.
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Handle Newton method convergence better for SparseCoding::OptimizeDictionary() and make maximum iterations a parameter.
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Known bug: CosineTree construction may fail in some cases on i386 systems (#358).
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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).
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Detect dependencies correctly when ARMA_USE_WRAPPER is not being defined (i.e., libarmadillo.so does not exist).
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Bugfix for compilation under Visual Studio (#348).
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GMM initialization is now safer and provides a working GMM when constructed with only the dimensionality and number of Gaussians (#301).
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Check for division by 0 in Forward-Backward Algorithm in HMMs (#301).
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Fix MaxVarianceNewCluster (used when re-initializing clusters for k-means) (#301).
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Fixed implementation of Viterbi algorithm in HMM::Predict() (#303).
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Significant speedups for dual-tree algorithms using the cover tree (#235, #314) including a faster implementation of FastMKS.
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Fix for LRSDP optimizer so that it compiles and can be used (#312).
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CF (collaborative filtering) now expects users and items to be zero-indexed, not one-indexed (#311).
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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().
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Removed incorrect PeriodicHRectBound (#58).
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Refactor LRSDP into LRSDP class and standalone function to be optimized (#305).
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Fix for centering in kernel PCA (#337).
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Added simulated annealing (SA) optimizer, contributed by Zhihao Lou.
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HMMs now support initial state probabilities; these can be set in the constructor, trained, or set manually with HMM::Initial() (#302).
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Added Nyström method for kernel matrix approximation by Marcus Edel.
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Kernel PCA now supports using Nyström method for approximation.
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Ball trees now work with dual-tree algorithms, via the BallBound<> bound structure (#307); fixed by Yash Vadalia.
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The NMF class is now AMF<>, and supports far more types of factorizations, by Sumedh Ghaisas.
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A QUIC-SVD implementation has returned, written by Siddharth Agrawal and based on older code from Mudit Gupta.
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Added perceptron and decision stump by Udit Saxena (these are weak learners for an eventual AdaBoost class).
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Sparse autoencoder added by Siddharth Agrawal.
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Memory leak in NeighborSearch index-mapping code fixed (#298).
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GMMs can be trained using the existing model as a starting point by specifying an additional boolean parameter to GMM::Estimate() (#296).
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Logistic regression implementation added in methods/logistic_regression (see also #293).
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L-BFGS optimizer now returns its function via Function().
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Version information is now obtainable via mlpack::util::GetVersion() or the __MLPACK_VERSION_MAJOR, __MLPACK_VERSION_MINOR, and __MLPACK_VERSION_PATCH macros (#297).
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Fix typos in allkfn and allkrann output.
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Cover tree support for range search (range_search), rank-approximate nearest neighbors (allkrann), minimum spanning tree calculation (emst), and FastMKS (fastmks).
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Dual-tree FastMKS implementation added and tested.
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Added collaborative filtering package (cf) that can provide recommendations when given users and items.
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Fix for correctness of Kernel PCA (kernel_pca) (#270).
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Speedups for PCA and Kernel PCA (#198).
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Fix for correctness of Neighborhood Components Analysis (NCA) (#279).
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Minor speedups for dual-tree algorithms.
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Fix for Naive Bayes Classifier (nbc) (#269).
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Added a ridge regression option to LinearRegression (linear_regression) (#286).
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Gaussian Mixture Models (gmm::GMM<>) now support arbitrary covariance matrix constraints (#283).
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MVU (mvu) removed because it is known to not work (#183).
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Minor updates and fixes for kernels (in mlpack::kernel).
- Minor bugfix so that FastMKS gets built.
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Speedups of cover tree traversers (#235).
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Addition of rank-approximate nearest neighbors (RANN), found in src/mlpack/methods/rann/.
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Addition of fast exact max-kernel search (FastMKS), found in src/mlpack/methods/fastmks/.
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Fix for EM covariance estimation; this should improve GMM training time.
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More parameters for GMM estimation.
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Force GMM and GaussianDistribution covariance matrices to be positive definite, so that training converges much more often.
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Add parameter for the tolerance of the Baum-Welch algorithm for HMM training.
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Fix for compilation with clang compiler.
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Fix for k-furthest-neighbor-search.
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Force minimum Armadillo version to 2.4.2.
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Better output of class types to streams; a class with a ToString() method implemented can be sent to a stream with operator<<.
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Change return type of GMM::Estimate() to double (#257).
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Style fixes for k-means and RADICAL.
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Handle size_t support correctly with Armadillo 3.6.2 (#258).
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Add locality-sensitive hashing (LSH), found in src/mlpack/methods/lsh/.
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Better tests for SGD (stochastic gradient descent) and NCA (neighborhood components analysis).
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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.
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NCA (neighborhood components analysis) now support an arbitrary optimizer (#245), including stochastic gradient descent (#249).
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Added density estimation trees, found in src/mlpack/methods/det/.
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Added non-negative matrix factorization, found in src/mlpack/methods/nmf/.
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Added experimental cover tree implementation, found in src/mlpack/core/tree/cover_tree/ (#157).
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Better reporting of boost::program_options errors (#225).
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Fix for timers on Windows (#212, #211).
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Fix for allknn and allkfn output (#204).
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Sparse coding dictionary initialization is now a template parameter (#220).
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Added kernel principal components analysis (kernel PCA), found in src/mlpack/methods/kernel_pca/ (#74).
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Fix for Lovasz-Theta AugLagrangian tests (#182).
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Fixes for allknn output (#185, #186).
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Added range search executable (#192).
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Adapted citations in documentation to BiBTeX; no citations in -h output (#195).
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Stop use of 'const char*' and prefer 'std::string' (#176).
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Support seeds for random numbers (#177).
- 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