A scalable, fast C++ machine learning library, with emphasis on usability.
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, LSH, 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.
Add serialization support for Perceptron and LogisticRegression.
Refactor SoftmaxRegression to predict into an arma::Row object, and add a softmax_regression program.
Refactor LSH to allow loading and saving of models.
ToString() is removed entirely (#487).
Add --input_model_file and --output_model_file options to appropriate machine learning algorithms.
Rename all executables to start with an "mlpack" prefix (#229).
See also https://mailman.cc.gatech.edu/pipermail/mlpack/2015-December/000706.html for more information.
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