All Versions
42
Latest Version
Avg Release Cycle
70 days
Latest Release
-

Changelog History
Page 2

  • v3.1.1

    May 27, 2019

    🚀 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).

  • v3.1.0

    April 26, 2019

    🚀 Released April 25, 2019.
    🚀 Release email

    ➕ 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).

  • v3.0.4

    November 13, 2018

    🚀 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).

  • v3.0.3

    July 29, 2018

    🚀 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).

  • v3.0.2

    June 09, 2018

    🚀 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).

  • v3.0.1

    May 11, 2018

    🚀 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).

  • v3.0.0

    March 31, 2018
    2018-03-30

    • 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.

  • v2.2.5

    August 26, 2017

    🚀 Released August 25th, 2017.

    • Compilation fix for some systems (#1082).
    • Fix PARAM_INT_OUT() (#1100).

  • v2.2.4

    July 19, 2017

    🚀 Released July 18th, 2017.

    • Speed and memory improvements for DBSCAN. --single_mode can now be used for situations where previously RAM usage was too high.
    • 🛠 Fix bug in CF causing incorrect recommendations.

  • v2.2.3

    May 24, 2017

    🚀 Released May 24th, 2017.

    • Bug fix for --predictions_file in mlpack_decision_tree program.