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@rcurtin rcurtin released this Nov 26, 2019 · 257 commits to master since this release

Released Nov. 26th, 2019.

  • Add valid and same padding option in Convolution and Atrous Convolution layer (#1988).
  • Add Model() to the FFN class to access individual layers (#2043).
  • Update documentation for pip and conda installation packages (#2044).
  • Add bindings for linear SVM (#1935); mlpack_linear_svm from the command-line, linear_svm() from Python.
  • Add support to return the layer name as std::string (#1987).
  • Speed and memory improvements for the Transposed Convolution layer (#1493).
  • Fix Windows Python build configuration (#1885).
  • Validate md5 of STB library after download (#2087).
  • Add __version__ to __init__.py (#2092).
Assets 2

@rcurtin rcurtin released this Nov 26, 2019 · 533 commits to master since this release

Released Oct. 1, 2019. (But I forgot to release it on Github; sorry about that.)

  • Enforce CMake version check for ensmallen #2032.
  • Fix CMake check for Armadillo version #2029.
  • Better handling of when STB is not installed #2033.
  • Fix Naive Bayes classifier computations in high dimensions #2022.
Assets 2

@rcurtin rcurtin released this Sep 26, 2019 · 558 commits to master since this release

Released Sept. 25, 2019.

  • Fix occasionally-failing RADICAL test (#1924).

  • Fix gcc 9 OpenMP compilation issue (#1970).

  • Added support for loading and saving of images (#1903).

  • Add Multiple Pole Balancing Environment (#1901, #1951).

  • Added functionality for scaling of data (#1876); see the command-line binding mlpack_preprocess_scale or Python binding preprocess_scale().

  • Add new parameter maximum_depth to decision tree and random forest bindings (#1916).

  • Fix prediction output of softmax regression when test set accuracy is calculated (#1922).

  • Pendulum environment now checks for termination. All RL environments now have an option to terminate after a set number of time steps (no limit by default) (#1941).

  • Add support for probabilistic KDE (kernel density estimation) error bounds when using the Gaussian kernel (#1934).

  • Fix negative distances for cover tree computation (#1979).

  • Fix cover tree building when all pairwise distances are 0 (#1986).

  • Improve KDE pruning by reclaiming not used error tolerance (#1954, #1984).

  • Optimizations for sparse matrix accesses in z-score normalization for CF (#1989).

  • Add kmeans_max_iterations option to GMM training binding gmm_train_main.

  • Bump minimum Armadillo version to 8.400.0 due to ensmallen dependency requirement (#2015).

Assets 2

@rcurtin rcurtin released this May 27, 2019 · 1203 commits to master since this release

Released May 26, 2019.

  • Fix random forest bug for numerical-only data (#1887).
  • Significant speedups for random forest (#1887).
  • Random forest now has minimum_gain_split and subspace_dim parameters (#1887).
  • Decision tree parameter print_training_error deprecated in favor of print_training_accuracy.
  • output option changed to predictions for adaboost and perceptron binding. Old options are now deprecated and will be preserved until mlpack 4.0.0 (#1882).
  • Concatenated ReLU layer (#1843).
  • Accelerate NormalizeLabels function using hashing instead of linear search (see src/mlpack/core/data/normalize_labels_impl.hpp) (#1780).
  • Add ConfusionMatrix() function for checking performance of classifiers (#1798).
  • Install ensmallen headers when it is downloaded during build (#1900).
Assets 2

@rcurtin rcurtin released this Apr 26, 2019 · 1425 commits to master since this release

Released April 25, 2019.
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  • Add DiagonalGaussianDistribution and DiagonalGMM classes to speed up the diagonal covariance computation and deprecate DiagonalConstraint (#1666).

  • Add kernel density estimation (KDE) implementation with bindings to other languages (#1301).

  • Where relevant, all models with a Train() method now return a double value representing the goodness of fit (i.e. final objective value, error, etc.) (#1678).

  • Add implementation for linear support vector machine (see src/mlpack/methods/linear_svm).

  • Change DBSCAN to use PointSelectionPolicy and add OrderedPointSelection (#1625).

  • Residual block support (#1594).

  • Bidirectional RNN (#1626).

  • Dice loss layer (#1674, #1714) and hard sigmoid layer (#1776).

  • output option changed to predictions and output_probabilities to probabilities for Naive Bayes binding (mlpack_nbc/nbc()). Old options are now deprecated and will be preserved until mlpack 4.0.0 (#1616).

  • Add support for Diagonal GMMs to HMM code (#1658, #1666). This can provide large speedup when a diagonal GMM is acceptable as an emission probability distribution.

  • Python binding improvements: check parameter type (#1717), avoid copying Pandas dataframes (#1711), handle Pandas Series objects (#1700).

Assets 2

@rcurtin rcurtin released this Nov 13, 2018 · 2931 commits to master since this release

Released November 13, 2018.

  • Bump minimum CMake version to 3.3.2.
  • CMake fixes for Ninja generator by Marc Espie (#1550, #1537, #1523).
  • More efficient linear regression implementation (#1500).
  • Serialization fixes for neural networks (#1508, #1535).
  • Mean shift now allows single-point clusters (#1536).
Assets 2

@rcurtin rcurtin released this Jul 29, 2018 · 2931 commits to master since this release

Released July 27th, 2018.

  • Fix Visual Studio compilation issue (#1443).
  • Allow running local_coordinate_coding binding with no initial_dictionary parameter when input_model is not specified (#1457).
  • Make use of OpenMP optional via the CMake USE_OPENMP configuration variable (#1474).
  • Accelerate FNN training by 20-30% by avoiding redundant calculations (#1467).
  • Fix math::RandomSeed() usage in tests (#1462, #1440).
  • Generate better Python setup.py with documentation (#1460).
Assets 2

@rcurtin rcurtin released this Jun 9, 2018 · 2931 commits to master since this release

Released June 8th, 2018.

  • Documentation generation fixes for Python bindings (#1421).
  • Fix build error for man pages if command-line bindings are not being built (#1424).
  • Add shuffle parameter and Shuffle() method to KFoldCV (#1412). This will shuffle the data when the object is constructed, or when Shuffle() is called.
  • Added neural network layers: AtrousConvolution (#1390), Embedding (#1401), and LayerNorm (layer normalization) (#1389).
  • Add Pendulum environment for reinforcement learning (#1388) and update Mountain Car environment (#1394).
Assets 2

@rcurtin rcurtin released this May 11, 2018 · 3055 commits to master since this release

Released May 10th, 2018.

  • Fix intermittently failing tests (#1387).
  • Add Big-Batch SGD (BBSGD) optimizer in src/mlpack/core/optimizers/bigbatch_sgd (#1131).
  • Fix simple compiler warnings (#1380, #1373).
  • Simplify NeighborSearch constructor and Train() overloads (#1378).
  • Add warning for OpenMP setting differences (#1358/#1382). When mlpack is compiled with OpenMP but another application linking against mlpack is not (or vice versa), a compilation warning will now be issued.
  • Restructured loss functions in src/mlpack/methods/ann/ (#1365).
  • Add environments for reinforcement learning tests (#1368, #1370, #1329).
  • Allow single outputs for multiple timestep inputs for recurrent neural networks (#1348).
  • Neural networks: add He and LeCun normal initializations (#1342), add FReLU and SELU activation functions (#1346, #1341), add alpha-dropout (#1349).
Assets 2

@rcurtin rcurtin released this Mar 31, 2018 · 3274 commits to master since this release

Released March 30th, 2018.

  • Speed and memory improvements for DBSCAN. --single_mode can now be used for situations where previously RAM usage was too high.
  • Bump minimum required version of Armadillo to 6.500.0.
  • Add automatically generated Python bindings. These have the same interface as the command-line programs.
  • Add deep learning infrastructure in src/mlpack/methods/ann/.
  • Add reinforcement learning infrastructure in src/mlpack/methods/reinforcement_learning/.
  • Add optimizers: AdaGrad, CMAES, CNE, FrankeWolfe, GradientDescent, GridSearch, IQN, Katyusha, LineSearch, ParallelSGD, SARAH, SCD, SGDR, SMORMS3, SPALeRA, SVRG.
  • Add hyperparameter tuning infrastructure and cross-validation infrastructure in src/mlpack/core/cv/ and src/mlpack/core/hpt/.
  • Fix bug in mean shift.
  • Add random forests (see src/mlpack/methods/random_forest).
  • Numerous other bugfixes and testing improvements.
  • Add randomized Krylov SVD and Block Krylov SVD.
Assets 2
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