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: A general purpose library to process and predict sequences of elements using echo state networks.

Initial Announcement on mloss.org.

About: "Pattern" is a web mining module for Python. It bundles tools for data retrieval, text analysis, clustering and classification, and data visualization.

• Small bug fixes in overall + performance improvements.
• Module pattern.web: updated to the new Bing API (Bing API has is paid service now).
• Module pattern.en: now includes Norvig's spell checking algorithm.
• Module pattern.de: new German tagger/chunker, courtesy of Schneider & Volk (1998) who kindly agreed to release their work in Pattern under BSD.
• Module pattern.search: the search syntax now includes { } syntax to define match groups.
• Module pattern.vector: fast implementation of information gain for feature selection.
• Module pattern.graph: now includes a toy semantic network of commonsense (see examples).
• Module canvas.js: image pixel effects & editor now supports live editing

About: The OrGanic Environment for Reservoir computing (Oger) toolbox is a Python toolbox for rapidly building, training and evaluating modular learning architectures on large datasets.

Initial Announcement on mloss.org.

About: This package contains a python and a matlab implementation of the most widely used algorithms for multi-armed bandit problems. The purpose of this package is to provide simple environments for comparison and numerical evaluation of policies.

Initial Announcement on mloss.org.

About: Nimfa is an open-source Python library that provides a unified interface to nonnegative matrix factorization algorithms. It includes implementations of state-of-the-art factorization methods, initialization approaches, and quality scoring. Both dense and sparse matrix representation are supported.

Initial Announcement on mloss.org.

About: mlpy is a Python module for Machine Learning built on top of NumPy/SciPy and of GSL.

New features:

• LibSvm(): pred_probability() now returns probability estimates; pred_values() added
• LibLinear(): pred_values() and pred_probability() added
• dtw_std: squared Euclidean option added
• LCS for series composed by real values (lcs_real()) added
• Documentation

Fix:

• wavelet submodule: cwt(): it returned only real values in morlet and poul
• IRelief(): remove np. in learn()
• fix rfe_kfda and rfe_w2 when p=1

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: Multicore/distributed large scale machine learning framework.

Update version.

About: A python implementation of Breiman's Random Forests.

Initial Announcement on mloss.org.

About: The Maja Machine Learning Framework (MMLF) is a general framework for problems in the domain of Reinforcement Learning (RL) written in python. It provides a set of RL related algorithms and a set of benchmark domains. Furthermore it is easily extensible and allows to automate benchmarking of different agents.

• Experiments can now be invoked from the command line
• Experiments can now be "scripted"
• MMLF Experimenter contains now basic module for statistical hypothesis testing
• MMLF Explorer can now visualize the model that has been learned by an agent

About: FLANN is a library for performing fast approximate nearest neighbor searches in high dimensional spaces. It contains a collection of algorithms we found to work best for nearest neighbor search.

See project page for changes.

About: The K-tree is a scalable approach to clustering inspired by the B+-tree and k-means algorithms.

Release of K-tree implementation in Python. This is targeted at a research and rapid prototyping audience.

About: The source code of the mldata.org site - a community portal for machine learning data sets.

Initial Announcement on mloss.org.

About: Tools to convert datasets from various formats to various formats, performance measures and API functions to communicate with mldata.org

• Change task file format, such that data splits can have a variable number items and put into up to 256 categories of training/validation/test/not used/...
• Various bugfixes.

About: This is the source code of the mloss.org website.

Now works with newer django versions and fixes several warnings and minor bugs underneath. The only user visible change is probably that the subscription and bookmark buttons work again.

About: A chatterbot that learns natural languages learning from imitation.

Alpha 1 - Codename: Wendell Borton ("Bllluuhhhhh...!!")

Short term memory greatly improved.

About: Obsolete. Use https://mloss.org/software/view/240/ instead.

0.6 release

About: Epistatic miniarray profiles (E-MAPs) are a high-throughput approach capable of quantifying aggravating or alleviating genetic interactions between gene pairs. The datasets resulting from E-MAP experiments typically take the form of a symmetric pairwise matrix of interaction scores. These datasets have a significant number of missing values - up to 35% - that can reduce the effectiveness of some data analysis techniques and prevent the use of others. This project contains nearest neighbor based tools for the imputation and prediction of these missing values. The code is implemented in Python and uses a nearest neighbor based approach. Two variants are used - a simple weighted nearest neighbors, and a local least squares based regression.

Initial Announcement on mloss.org.