PyMVPA is a Python module intended to ease pattern classification analyzes of large datasets. PyMVPA provides high-level abstraction of typical processing steps and a number of implementations of some popular algorithms.

While it is not limited to the neuroimaging domain, it is eminently suited for such datasets. PyMVPA is truely free software (in every respect) and additionally requires nothing but free-software to run.

PyMVPA stands for MultiVariate Pattern Analysis (MVPA) in Python.

What's New in This Release: [ read full changelog ]

New functionality (19 NF commits):
· GNB implements Gaussian Naïve Bayes Classifier.
· read_fsl_design() to read FSL FEAT design.fsf files (Contributed by Russell A. Poldrack).
· SequenceStats to provide basic statistics on labels sequence (counter-balancing, autocorrelation).
· New exceptions DegenerateInputError and FailedToTrainError to be thrown by classifiers primarily during training/testing.
· Debug target STATMC to report on progress of Monte-Carlo sampling (during permutation testing).

Refactored (15 RF commits):
· To get users prepared to 0.5 release, internally and in some examples/documentation, access to states and parameters is done via corresponding collections, not from the top level object (e.g. clf.states.predictions instead of soon-to-be-deprecated clf.predictions). That should lead also to improved performance.
· Adopted copy.py from python2.6 (support Ellipsis as well).

Fixed (38 BF commits):
· GLM output does not depend on the enabled states any more.
· Variety of docstrings fixed and/or improved.
· Do not derive NaN scaling for SVM’s C whenever data is degenerate (lead to never finishing SVM training).
sg :
· KRR is optional now – avoids crashing if KRR is not available.
· tolerance to absent set_precompute_matrix in svmlight in recent shogun versions.
· support for recent (present in 0.9.1) API change in exposing debug levels.
· Python 2.4 compatibility issues: kNN and IFS