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mlpack 1.1.0

????-??-??

  • 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, 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.

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 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