Mlpack

2019-11-26
* 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).

* Correctly handle RNN sequences that are shorter than the value of rho (2102).

2019-10-01
* 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).

2019-09-25
* Fix some potential infinity errors in Naive Bayes Classifier (2022).

* 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).

2019-05-26
* 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).

2019-04-25
* 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).

2018-11-13
* Bump minimum CMake version to 3.3.2.

* CMake fixes for Ninja generator by Marc Espie.