About: The Universal Java Matrix Package (UJMP) is a data processing tool for Java. Unlike JAMA and Colt, it supports multi-threading and is therefore much faster on current hardware. It does not only support matrices with double values, but instead handles every type of data as a matrix through a common interface, e.g. CSV files, Excel files, images, WAVE audio files, tables in SQL data bases, and much more.

Updated to version 0.3.0

About: A Matlab implementation of Multilinear PCA (MPCA) and MPCA+LDA for dimensionality reduction of tensor data with sample code on gait recognition

1. The MPCA paper is updated with a typo (the MAD measure in Table II) corrected.

2. Tensor toolbox version 2.1 is included for convenience.

3. Full code on gait recognition is included for verification and comparison.

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 Matlab implementation of Uncorrelated Multilinear PCA (UMPCA) for dimensionality reduction of tensor data via tensor-to-vector projection

Initial Announcement on mloss.org.

About: This archive contains a Matlab implementation of the Multilinear Principal Component Analysis (MPCA) algorithm and MPCA+LDA, as described in the paper Haiping Lu, K.N. Plataniotis, and A.N. Venetsanopoulos, "MPCA: Multilinear Principal Component Analysis of Tensor Objects", IEEE Transactions on Neural Networks, Vol. 19, No. 1, Page: 18-39, January 2008.

Initial Announcement on mloss.org.

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: This software is aimed at performing supervised/unsupervised learning on graph data, where each graph is represented as binary indicators of subgraph features.

Initial Announcement on mloss.org.