259 lines
8.6 KiB
Markdown
259 lines
8.6 KiB
Markdown
### mlpack 1.1.0
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###### ????-??-??
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* Removed overclustering support from k-means because it is not well-tested,
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may be buggy, and is (I think) unused. If this was support you were using,
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open a bug or get in touch with us; it would not be hard for us to
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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,
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Pelleg-Moore's algorithm, and the DTNN (dual-tree nearest neighbor)
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algorithm.
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* Significant acceleration of LRSDP via the use of accu(a % b) instead of
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trace(a * b).
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* Added MatrixCompletion class (matrix_completion), which performs nuclear
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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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### mlpack 1.0.11
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###### 2014-12-11
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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
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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
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versions.
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* Handle Newton method convergence better for
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SparseCoding::OptimizeDictionary() and make maximum iterations a parameter.
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* Known bug: CosineTree construction may fail in some cases on i386 systems.
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(#376)
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### mlpack 1.0.10
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###### 2014-08-29
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* Bugfix for NeighborSearch regression which caused very slow allknn/allkfn.
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Speeds are now restored to approximately 1.0.8 speeds, with significant
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improvement for the cover tree.
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* Detect dependencies correctly when ARMA_USE_WRAPPER is not being defined
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(i.e., libarmadillo.so does not exist).
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* Bugfix for compilation under Visual Studio.
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### mlpack 1.0.9
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###### 2014-07-28
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* GMM initialization is now safer and provides a working GMM when constructed
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with only the dimensionality and number of Gaussians (#314).
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* Check for division by 0 in Forward-Backward Algorithm in HMMs (#314).
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* Fix MaxVarianceNewCluster (used when re-initializing clusters for k-means)
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(#314).
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* Fixed implementation of Viterbi algorithm in HMM::Predict() (#316).
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* Significant speedups for dual-tree algorithms using the cover tree (#243, #329) including a faster implementation of FastMKS.
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* Fix for LRSDP optimizer so that it compiles and can be used (#325).
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* CF (collaborative filtering) now expects users and items to be zero-indexed,
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not one-indexed (#324).
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* CF::GetRecommendations() API change: now requires the number of
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recommendations as the first parameter. The number of users in the local
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neighborhood should be specified with CF::NumUsersForSimilarity().
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* Removed incorrect PeriodicHRectBound (#30).
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* Refactor LRSDP into LRSDP class and standalone function to be optimized
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(#318).
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* Fix for centering in kernel PCA (#355).
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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
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constructor, trained, or set manually with HMM::Initial() (#315).
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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
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structure (#320); fixed by Yash Vadalia.
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* The NMF class is now AMF<>, and supports far more types of factorizations,
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by Sumedh Ghaisas.
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* A QUIC-SVD implementation has returned, written by Siddharth Agrawal and
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based on older code from Mudit Gupta.
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* Added perceptron and decision stump by Udit Saxena (these are weak learners
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for an eventual AdaBoost class).
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* Sparse autoencoder added by Siddharth Agrawal.
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### mlpack 1.0.8
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###### 2014-01-06
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* Memory leak in NeighborSearch index-mapping code fixed (#310).
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* GMMs can be trained using the existing model as a starting point by
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specifying an additional boolean parameter to GMM::Estimate() (#308).
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* Logistic regression implementation added in methods/logistic_regression (see
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also #305).
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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
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__MLPACK_VERSION_MAJOR, __MLPACK_VERSION_MINOR, and __MLPACK_VERSION_PATCH
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macros (#309).
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* Fix typos in allkfn and allkrann output.
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### mlpack 1.0.7
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###### 2013-10-04
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* Cover tree support for range search (range_search), rank-approximate nearest
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neighbors (allkrann), minimum spanning tree calculation (emst), and FastMKS
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(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
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when given users and items.
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* Fix for correctness of Kernel PCA (kernel_pca) (#280).
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* Speedups for PCA and Kernel PCA (#204).
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* Fix for correctness of Neighborhood Components Analysis (NCA) (#289).
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* Minor speedups for dual-tree algorithms.
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* Fix for Naive Bayes Classifier (nbc) (#279).
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* Added a ridge regression option to LinearRegression (linear_regression)
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(#298).
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* Gaussian Mixture Models (gmm::GMM<>) now support arbitrary covariance matrix
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constraints (#294).
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* MVU (mvu) removed because it is known to not work (#189).
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* Minor updates and fixes for kernels (in mlpack::kernel).
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### mlpack 1.0.6
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###### 2013-06-13
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* Minor bugfix so that FastMKS gets built.
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### mlpack 1.0.5
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###### 2013-05-01
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* Speedups of cover tree traversers (#243).
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* Addition of rank-approximate nearest neighbors (RANN), found in
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src/mlpack/methods/rann/.
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* Addition of fast exact max-kernel search (FastMKS), found in
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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
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definite, so that training converges much more often.
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* Add parameter for the tolerance of the Baum-Welch algorithm for HMM
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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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### mlpack 1.0.4
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###### 2013-02-08
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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
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implemented can be sent to a stream with operator<<. See #164.
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* Change return type of GMM::Estimate() to double (#266).
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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 (#267).
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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
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components analysis).
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### mlpack 1.0.3
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###### 2012-09-16
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* Remove internal sparse matrix support because Armadillo 3.4.0 now includes
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it. When using Armadillo versions older than 3.4.0, sparse matrix support
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is not available.
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* NCA (neighborhood components analysis) now support an arbitrary optimizer
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(#254), including stochastic gradient descent (#258).
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### mlpack 1.0.2
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###### 2012-08-15
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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
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src/mlpack/core/tree/cover_tree/ (#156).
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* Better reporting of boost::program_options errors (#231).
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* Fix for timers on Windows (#218, #217).
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* Fix for allknn and allkfn output (#210).
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* Sparse coding dictionary initialization is now a template parameter (#226).
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### mlpack 1.0.1
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###### 2012-03-03
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* Added kernel principal components analysis (kernel PCA), found in
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src/mlpack/methods/kernel_pca/ (#47).
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* Fix for Lovasz-Theta AugLagrangian tests (#188).
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* Fixes for allknn output (#191, #192).
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* Added range search executable (#198).
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* Adapted citations in documentation to BiBTeX; no citations in -h output
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(#201).
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* Stop use of 'const char*' and prefer 'std::string' (#183).
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* Support seeds for random numbers (#182).
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### mlpack 1.0.0
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###### 2011-12-17
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* Initial release. See any resolved tickets numbered less than #196 or
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execute this query:
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http://www.mlpack.org/trac/query?status=closed&milestone=mlpack+1.0.0
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