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Changelog History
Page 4

  • v1.0.11 Changes

    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 the 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).

  • v1.0.10 Changes

    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).

  • v1.0.9 Changes

    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.

  • v1.0.8 Changes

    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.

  • v1.0.7 Changes

    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).

  • v1.0.6 Changes

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

  • v1.0.5 Changes

    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.

  • v1.0.4 Changes

    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).

  • v1.0.3 Changes

    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).

  • v1.0.2 Changes

    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).