Changelog History
Page 2
-
v3.1.1
May 27, 2019๐ Released May 26, 2019.
- ๐ Fix random forest bug for numerical-only data (#1887).
- Significant speedups for random forest (#1887).
- Random forest now has
minimum_gain_split
andsubspace_dim
parameters (#1887). - Decision tree parameter
print_training_error
deprecated in favor ofprint_training_accuracy
. - ๐
output
option changed topredictions
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). - โ Add
ConfusionMatrix()
function for checking performance of classifiers (#1798). - ๐ Install ensmallen headers when it is downloaded during build (#1900).
-
v3.1.0
April 26, 2019๐ Released April 25, 2019.
๐ Release emailโ Add DiagonalGaussianDistribution and DiagonalGMM classes to speed up the diagonal covariance computation and deprecate DiagonalConstraint (#1666).
โ Add kernel density estimation (KDE) implementation with bindings to other languages (#1301).
Where relevant, all models with a
Train()
method now return adouble
value representing the goodness of fit (i.e. final objective value, error, etc.) (#1678).โ Add implementation for linear support vector machine (see
src/mlpack/methods/linear_svm
).๐ Change DBSCAN to use PointSelectionPolicy and add OrderedPointSelection (#1625).
๐ Residual block support (#1594).
Bidirectional RNN (#1626).
Dice loss layer (#1674, #1714) and hard sigmoid layer (#1776).
output
option changed topredictions
andoutput_probabilities
toprobabilities
for Naive Bayes binding (mlpack_nbc
/nbc()
). Old options are now deprecated and will be preserved until mlpack 4.0.0 (#1616).โ 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, 2018๐ Released November 13, 2018.
-
v3.0.3
July 29, 2018๐ Released July 27th, 2018.
- ๐ Fix Visual Studio compilation issue (#1443).
- Allow running local_coordinate_coding binding with no initial_dictionary parameter when input_model is not specified (#1457).
- ๐ง Make use of OpenMP optional via the CMake USE_OPENMP configuration variable (#1474).
- Accelerate FNN training by 20-30% by avoiding redundant calculations (#1467).
- ๐ Fix math::RandomSeed() usage in tests (#1462, #1440).
- ๐ Generate better Python setup.py with documentation (#1460).
-
v3.0.2
June 09, 2018๐ Released June 8th, 2018.
- ๐ Documentation generation fixes for Python bindings (#1421).
- ๐ Fix build error for man pages if command-line bindings are not being built (#1424).
- โ Add shuffle parameter and Shuffle() method to KFoldCV (#1412). This will shuffle the data when the object is constructed, or when Shuffle() is called.
- โ Added neural network layers: AtrousConvolution (#1390), Embedding (#1401), and LayerNorm (layer normalization) (#1389).
- โ Add Pendulum environment for reinforcement learning (#1388) and update Mountain Car environment (#1394).
-
v3.0.1
May 11, 2018๐ Released May 10th, 2018.
- ๐ Fix intermittently failing tests (#1387).
- โ Add Big-Batch SGD (BBSGD) optimizer in src/mlpack/core/optimizers/bigbatch_sgd (#1131).
- ๐ Fix simple compiler warnings (#1380, #1373).
- Simplify NeighborSearch constructor and Train() overloads (#1378).
- โ 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).
- โ Add environments for reinforcement learning tests (#1368, #1370, #1329).
- ๐ 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, 20182018-03-30
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.
-
v2.2.4
July 19, 2017๐ 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.
- ๐ Fix bug in CF causing incorrect recommendations.
-
v2.2.3
May 24, 2017๐ Released May 24th, 2017.
- Bug fix for --predictions_file in mlpack_decision_tree program.