About: This is a large scale online learning implementation with several useful features. See the webpage for more details.

Initial Announcement on mloss.org.

About: MDP is a Python library of widely used data processing algorithms that can be combined according to a pipeline analogy to build more complex data processing software. The base of available algorithms includes signal processing methods (Principal Component Analysis, Independent Component Analysis, Slow Feature Analysis), manifold learning methods ([Hessian] Locally Linear Embedding), several classifiers, probabilistic methods (Factor Analysis, RBM), data pre-processing methods, and many others.

What's new in version 3.3?

• support sklearn versions up to 0.12
• cleanly support reload
• fail gracefully if pp server does not start
• several bug-fixes and improvements

About: LIBSVM is an integrated software for support vector classification, (C-SVC, nu-SVC ), regression (epsilon-SVR, nu-SVR) and distribution estimation (one-class SVM). It supports multi-class [...]

Initial Announcement on mloss.org.

About: Armadillo is a high quality C++ library for linear algebra & scientific computing, aiming towards a good balance between speed and ease of use. The function syntax is deliberately similar to MATLAB. Useful for algorithm development directly in C++, or quick conversion of research code into production environments (eg. software & hardware products).

• faster handling of sparse matrices by kron() and repmat()
• faster transpose of sparse matrices
• faster element access in sparse matrices
• faster row iterators for sparse matrices
• faster handling of compound expressions by trace()
• more efficient handling of aliasing in submatrix views
• expanded normalise() to handle sparse matrices
• expanded .transform() and .for_each() to handle sparse matrices
• added reverse() for reversing order of elements
• added repelem() for replicating elements
• added roots() for finding the roots of a polynomial

About: Lush is an object-oriented Lisp dialect with a super-simple way of integrating C/C++ code and libraries. It includes extensive libraries for numerical computing, machine learning, and computer [...]

Initial Announcement on mloss.org.

About: Universal Python-written numerical optimization toolbox. Problems: NLP, LP, QP, NSP, MILP, LSP, LLSP, MMP, GLP, SLE, MOP etc; general logical constraints, categorical variables, automatic differentiation, stochastic programming, interval analysis, many other goodies

http://openopt.org/Changelog

About: Orange is a component-based machine learning and data mining software. It includes a friendly yet powerful and flexible graphical user interface for visual programming. For more advanced use(r)s, [...]

The core of the system (except the GUI) no longer includes any GPL code and can be licensed under the terms of BSD upon request. The graphical part remains under GPL.

Changed the BibTeX reference to the paper recently published in JMLR MLOSS.

About: Python module to ease pattern classification analyses of large datasets. It provides high-level abstraction of typical processing steps (e.g. data preparation, classification, feature selection, [...]

• 2.0.0 (Mon, Dec 19 2011)

This release aggregates all the changes occurred between official releases in 0.4 series and various snapshot releases (in 0.5 and 0.6 series). To get better overview of high level changes see :ref:release notes for 0.5 <chap_release_notes_0.5> and :ref:0.6 <chap_release_notes_0.6> as well as summaries of release candidates below

• Fixes (23 BF commits)

  • significance level in the right tail was fixed to include the value tested -- otherwise resulted in optimistic bias (or absurdly high significance in improbable case if all estimates having the same value)
  • compatible with the upcoming IPython 0.12 and renamed sklearn (Fixes #57)
  • do not double-train slave classifiers while assessing sensitivities (Fixes #53)

• Enhancements (30 ENH + 3 NF commits)

  • resolving voting ties in kNN based on mean distance, and randomly in SMLR
  • :class:kNN's ca.estimates now contains dictionaries with votes for each class
  • consistent zscoring in :class:Hyperalignment

• 2.0.0~rc5 (Wed, Oct 19 2011)

• Major: to allow easy co-existence of stable PyMVPA 0.4.x, 0.6 development mvpa module was renamed into mod:mvpa2.

• Fixes

  • compatible with the new Shogun 1.x series
  • compatible with the new h5py 2.x series
  • mvpa-prep-fmri -- various compatibility fixes and smoke testing
  • deepcopying :class:SummaryStatistics during add

• Enhancements

  • tutorial uses :mod:mvpa2.tutorial_suite now
  • better suppression of R warnings when needed
  • internal attributes of many classes were exposed as properties
  • more unification of __repr__ for many classes

• 0.6.0~rc4 (Wed, Jun 14 2011)

• Fixes

  • Finished transition to :mod:nibabel conventions in plot_lightbox
  • Addressed :mod:matplotlib.hist API change
  • Various adjustments in the tests batteries (:mod:nibabel 1.1.0 compatibility, etc)

• New functionality

  • Explicit new argument flatten to from_wizard -- default behavior changed if mapper was provided as well

• Enhancements

  • Elaborated __str__ and __repr__ for some Classifiers and Measures

• 0.6.0~rc3 (Thu, Apr 12 2011)

• Fixes

  • Bugfixes regarding the interaction of FlattenMapper and BoxcarMapper that affected event-related analyses.
  • Splitter now handles attribute value None for splitting properly.
  • GNBSearchlight handling of
    roi_ids.
  • More robust detection of mod:scikits.learn and :mod:nipy externals.

• New functionality

  • Added a Repeater node to yield a dataset multiple times and
    Sifter node to exclude some datasets. Consequently, the "nosplitting" mode of Splitter got removed at the same time.
  • :file:tools/niils -- little tool to list details (dimensionality, scaling, etc) of the files in nibabel-supported formats.

• Enhancements

  • Numerous documentation fixes.
  • Various improvements and increased flexibility of null distribution estimation of Measures.
  • All attribute are now reported in sorted order when printing a dataset.
  • fmri_dataset now also stores the input image type.
  • Crossvalidation can now take a custom Splitter instance. Moreover, the default splitter of CrossValidation is more robust in terms of number and type of created splits for common usage patterns (i.e. together with partitioners).
  • CrossValidation takes any custom Node as errorfx argument.
  • ConfusionMatrix can now be used as an errorfx in Crossvalidation.
  • LOE(ACC): Linear Order Effect in ACC was added to
    ConfusionMatrix to detect trends in performances across splits.
  • A Node s postproc is now accessible as a property.
  • RepeatedMeasure has a new 'concat_as' argument that allows results to be concatenated along the feature axis. The default behavior, stacking as multiple samples, is unchanged.
  • Searchlight now has the ability to mark the center/seed of an ROI in with a feature attribute in the generated datasets.
  • debug takes args parameter for delayed string comprehensions. It should reduce run-time impact of debug() calls in regular, non -O mode of Python operation.
  • String summaries and representations (provided by __str__ and __repr__) were made more exhaustive and more coherent. Additional properties to access initial constructor arguments were added to variety of classes.

• Internal changes

  • New debug target STDOUT to allow attaching metrics (e.g. traceback, timestamps) to regular output printed to stdout

  • New set of decorators to help with unittests

  • @nodebug to disable specific debug targets for the duration of the test.

  • @reseed_rng to guarantee consistent random data given initial seeding.

  • @with_tempfile to provide a tempfile name which would get removed upon completion (test success or failure)

  • Dropping daily testing of maint/0.5 branch -- RIP.

  • Collection s were provided with adequate (deep|)copy. And Dataset was refactored to use Collection s copy method.

  • update-* Makefile rules automatically should fast-forward corresponding website-updates branch

  • MVPA_TESTS_VERBOSITY controls also :mod:numpy warnings now.

  • Dataset.__array__ provides original array instead of copy (unless dtype is provided)

Also adapts changes from 0.4.6 and 0.4.7 (see corresponding changelogs).

• 0.6.0~rc2 (Thu, Mar 3 2011)

• Various fixes in the mvpa.atlas module.

• 0.6.0~rc1 (Thu, Feb 24 2011)

• Many, many, many

• For an overview of the most drastic changes :ref:see constantly evolving release notes for 0.6 <chap_release_notes_0.6>

• 0.5.0 (sometime in March 2010)

This is a special release, because it has never seen the general public. A summary of fundamental changes introduced in this development version can be seen in the :ref:release notes <chap_release_notes_0.5>.

Most notably, this version was to first to come with a comprehensive two-day workshop/tutorial.

• 0.4.7 (Tue, Mar 07 2011) (Total: 12 commits)

A bugfix release

• Fixed

  • Addressed the issue with input NIfTI files having scl_ fields set: it could result in incorrect analyses and map2nifti-produced NIfTI files. Now input files account for scaling/offset if scl_ fields direct to do so. Moreover upon map2nifti, those fields get reset.
  • :file:doc/examples/searchlight_minimal.py - best error is the minimal one

• Enhancements

  • :class:~mvpa.clfs.gnb.GNB can now tolerate training datasets with a single label
  • :class:~mvpa.clfs.meta.TreeClassifier can have trailing nodes with no classifier assigned

• 0.4.6 (Tue, Feb 01 2011) (Total: 20 commits)

A bugfix release

• Fixed (few BF commits):

  • Compatibility with numpy 1.5.1 (histogram) and scipy 0.8.0 (workaround for a regression in legendre)
  • Compatibility with libsvm 3.0
  • :class:~mvpa.clfs.plr.PLR robustification

• Enhancements

  • Enforce suppression of numpy warnings while running unittests. Also setting verbosity >= 3 enables all warnings (Python, NumPy, and PyMVPA)
  • :file:doc/examples/nested_cv.py example (adopted from 0.5)
  • Introduced base class :class:~mvpa.clfs.base.LearnerError for classifiers' exceptions (adopted from 0.5)
  • Adjusted example data to live upto nibabel's warranty of NIfTI standard-compliance
  • More robust operation of MC iterations -- skip iterations where classifier experienced difficulties and raise an exception (e.g. due to degenerate data)

About: The SGD-2.0 package contains implementations of the SGD and ASGD algorithms for linear SVMs and linear CRFs.

Version 2.0 features ASGD.

About: kernlab provides kernel-based Machine Learning methods for classification, regression, clustering, novelty detection, quantile regression and dimensionality reduction. Among other methods kernlab [...]

minor fixes in kcca and ksvm functions

About: C-MixSim is an open source package written in C for simulating finite mixture models with Gaussian components. With a vast number of clustering algorithms, evaluating performance is important. C-MixSim provides an easy and convenient way of generating datasets from Gaussian mixture models with different levels of clustering complexity. C-MixSim is released under the GNU GPL license.

Initial Announcement on mloss.org.

About: A K-means clustering implementation for command-line, Python, Matlab and C. This algorithm yields the very same solution as standard Kmeans, even after each iteration. However it uses some triangle [...]

Initial Announcement on mloss.org.

About: Torch5 provides a matlab-like environment for state-of-the-art machine learning algorithms. It is easy to use and provides a very efficient implementation, thanks to a easy and fast scripting [...]

Initial Announcement on mloss.org.

About: The Open Computer Vision Library is a collection of algorithms and sample code for various computer vision problems. The library is compatible with IPL and utilizes Intel Integrated Performance [...]

Initial Announcement on mloss.org.

About: This is a C++ software designed to train large-scale SVMs for binary classification. The algorithm is also implemented in parallel (**PGPDT**) for distributed memory, strictly coupled multiprocessor [...]

Initial Announcement on mloss.org.

About: PLearn is a large C++ machine-learning library with a set of Python tools and Python bindings. It is mostly a research platform for developing novel algorithms, and is being used extensively at [...]

Initial Announcement on mloss.org.