mlpack 3.3.1

Released April 29th, 2020.

• Minor Julia and Python documentation fixes (#2373).

• Updated terminal state and fixed bugs for Pendulum environment (#2354, #2369).

• Added EliSH activation function (#2323).

• Add L1 Loss function (#2203).

• Pass CMAKE_CXX_FLAGS (compilation options) correctly to Python build (#2367).

• Expose ensmallen Callbacks for sparseautoencoder (#2198).

• Bugfix for LARS class causing invalid read (#2374).

• Add serialization support from Julia; use mlpack.serialize() and mlpack.deserialize() to save and load from IOBuffers.

mlpack 3.3.0

Released April 7th, 2020.

• Templated return type of Forward function of loss functions (#2339).

• Added R2 Score regression metric (#2323).

• Added mean squared logarithmic error loss function for neural networks (#2210).

• Added mean bias loss function for neural networks (#2210).

• The DecisionStump class has been marked deprecated; use the DecisionTree class with NoRecursion=true or use ID3DecisionStump instead (#2099).

• Added probabilities_file parameter to get the probabilities matrix of AdaBoost classifier (#2050).

• Fix STB header search paths (#2104).

• Add DISABLE_DOWNLOADS CMake configuration option (#2104).

• Add padding layer in TransposedConvolutionLayer (#2082).

• Fix pkgconfig generation on non-Linux systems (#2101).

• Use log-space to represent HMM initial state and transition probabilities (#2081).

• Add functions to access parameters of Convolution and AtrousConvolution layers (#1985).

• Add Compute Error function in lars regression and changing Train function to return computed error (#2139).

• Add Julia bindings (#1949). Build settings can be controlled with the BUILD_JULIA_BINDINGS=(ON/OFF) and JULIA_EXECUTABLE=/path/to/julia CMake parameters.

• CMake fix for finding STB include directory (#2145).

• Add bindings for loading and saving images (#2019); mlpack_image_converter from the command-line, mlpack.image_converter() from Python.

• Add normalization support for CF binding (#2136).

• Add Mish activation function (#2158).

• Update init_rules in AMF to allow users to merge two initialization rules (#2151).

• Add GELU activation function (#2183).

• Better error handling of eigendecompositions and Cholesky decompositions (#2088, #1840).

• Add LiSHT activation function (#2182).

• Add Valid and Same Padding for Transposed Convolution layer (#2163).

• Add CELU activation function (#2191)

• Add Log-Hyperbolic-Cosine Loss function (#2207)

• Change neural network types to avoid unnecessary use of rvalue references (#2259).

• Bump minimum Boost version to 1.58 (#2305).

• Refactor STB support so HAS_STB macro is not needed when compiling against mlpack (#2312).

• Add Hard Shrink Activation Function (#2186).

• Add Soft Shrink Activation Function (#2174).

• Add Hinge Embedding Loss Function (#2229).

• Add Cosine Embedding Loss Function (#2209).

• Add Margin Ranking Loss Function (#2264).

• Bugfix for incorrect parameter vector sizes in logistic regression and softmax regression (#2359).

mlpack 3.2.1

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.

mlpack 3.2.0

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).

mlpack 3.1.1

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).

mlpack 3.1.0

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).

mlpack 3.0.4

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).

mlpack 3.0.3

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).

mlpack 3.0.2

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).

mlpack 3.0.1

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).