Release Notes: The most important change is the inclusion of eigen in the source distribution, which makes milk easier to compile. In addition, this release adds subspace projection k-nearest neighbours and mds_dists functionality.
Release Notes: This release fixes a bug in adaboost and adds a few extra small functions such as zscoring on multiple axes, Euclidean multi-dimensional scaling, and tree-based multi-class learning.
Release Notes: Interfaces are more consistent (learners ignore arguments they cannot use and the default model supports the apply_many method). There are many improvements and bugfixes.
Release Notes: The new milk.ext.jugparallel module was added to interface with jug (http://luispedro.org/software/jug). This makes it easy to parallelize things such as n-fold cross validation (each fold runs on its own processor) or multiple kmeans random starts. Some new functions were added: measures.curves.precision_recall, milk.unsupervised.kmeans.select_best.kmeans. A tricky bug in SDA and a few minor issues elsewhere were fixed.
Release Notes: Many speed improvements. Some bugfixes (to gridminimize and tree learning). A few new utility functions.
Release Notes: Compilation on Windows was fixed.
Release Notes: Logistic regression was added. Demos are included in the source and documentation. Cluster agreement metrics were added. An nfoldcrossvalidation bug when using the origins parameter was fixed.
Release Notes: New features: unsupervised (1-class) kernel density modeling, a weights option to some learners, stump learner, and Adaboost. A fix for when SDA returns empty.
Release Notes: A fix was included for 64-bit machines. Functions in measures.py have a new interface.
Release Notes: Random forest learners were added. Decision trees were sped up by 20 times. Gridsearch is much faster since it finds an optimum without computing all folds.