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.. index:: changelog

PyMVPA Development Changelog

This changelog only lists rather macroscopic changes to PyMVPA. The full VCS changelog is available here:

http://git.debian.org/?p=pkg-exppsy/pymvpa.git;a=summary

In addition there is also a somewhat unconventional visual changelog:

http://www.pymvpa.org/history.html

'Closes' statement IDs refer to the Debian bug tracking system and can be queried by visiting the URL:

http://bugs.debian.org/<bug id>
Unreleased changes

Changes described here are not yet released, but available from VCS repository.

Releases

  • 0.4.3 (Sat, 5 Sep 2009) (Total: 165 commits)
  • 0.4.2 (Mon, 25 May 2009)
  • 0.4.1 (Sat, 24 Jan 2009)
  • 0.4.0 (Sat, 15 Nov 2008)
    • Add :class:`~mvpa.misc.io.hamster.Hamster`, as a simple facility to easily store any serializable objects in a compressed file and later on resurrect all of them with a single line of code.
    • SVM backend is now configurable via :envvar:`MVPA_SVM_BACKEND` (libsvm or shogun).
    • Non-deterministic tests in the unittest battery are now configurable via :envvar:`MVPA_TESTS_LABILE`.
    • New helper to determine and plot the best matching distribution(s) for the data (matchDistribution, plotDistributionMatches). It is WiP thus API can change in the upcoming release.
    • Simplifies API of mappers.
    • Splitters can now limit the number of splits automatically.
    • New :class:`~mvpa.mappers.base.CombinedMapper` to map between multiple, independent dataspace and a common feature space.
    • New :class:`~mvpa.mappers.base.ChainMapper` to create chains of mappers of abitrary lenght (e.g. to build preprocessing pipelines).
    • New :class:`~mvpa.datasets.event.EventDataset` to rapidly extract boxcar-shaped samples from data array using a simple list of :class:`~mvpa.misc.support.Event` definitions.
    • Removed obsolete MetricMapper class. :class:`~mvpa.mappers.base.Mapper` itself provides the facilities for dealing with metrics.
    • :class:`~mvpa.mappers.boxcar.BoxcarMapper` can now handle data with more than four dimensions/axis and also performs reverse mapping of single boxcar samples.
    • :class:`~mvpa.misc.fsl.base.FslEV3` can now convert EV3 files into a list of :class:`~mvpa.misc.support.Event` instances.
    • Results of tests for external dependencies are now stored in PyMVPA's config manager (mvpa.cfg) and can be stored to a file (not done automatically at the moment). This will significantly decrease the time needed to import the mvpa module, as it prevents the repeated and lengthy tests for working externals.
    • Initial support for ROC computing and AUC as an accuracy measure.
    • Weights of LARS are now available via :class:`~mvpa.clfs.lars.LARSWeights`.
    • Added an initial list of MVPA-related references to the manual, tagged with keywords and comments as well is DOI or similar URL reference to the original document.
    • Added initial glossary to the manual.
    • New 'Module reference', as a middle-ground between manual and API reference.
    • New manual section about meta-classifiers (contributed by James M. Hughes).
    • New minimal example for a 'getting started' section in the manual.
    • Former :envvar:`MVPA_QUICKTEST` was renamed to :envvar:`MVPA_TESTS_QUICK`.
    • Update installation instructions for RPM-based distributions to make use of the OpenSUSE Build Service.
    • Updated install instructions for several RPM-based GNU/Linux distributions.
    • Switch from distutils to numpy.distutils (no change in dependencies).
    • Depend on PyNIfTI >= 0.20081017.1 and gain a smaller memory footprint when accessing NIfTI files via all datasets with NIfTI support.
    • Added workaround to make PyMVPA work with older Shogun releases and those from 0.6.4 on, which introduced backward-incompatible API changes.
  • 0.3.1 (Sun, 14 Sep 2008)
  • 0.3.0 (Mon, 18 Aug 2008)
    • Import of binary EEP files (used by EEProbe) and EEPDataset class.
    • Initial version of a meta dataset class (MetaDataset). This is a container for multiple datasets, which behaves like a dataset itself.
    • Regression performance is summarized now within RegressionStatistics.
    • Error functions: CorrErrorPFx, RelativeRMSErrorFx.
    • Measures: CorrCoef.
    • Data generators: chirp, wr1996
    • Few more examples: curvefitting, kerneldemo, smellit, projections
    • Updated kNN classifier. kNN is now able to use custom distance function to determine that nearest neighbors. It also (re)gained the ability to do simple majority or weighted voting.
    • Some initial convenience functions for plotting typical results and data exploration.
    • Unified configuration handling with support for user-specific and analysis-specific config files, as well as the ability to override all config settings via environment variables. The configuration handling is used for PyMVPA internal settings, but can also be easily used for custom (user-)settings.
    • Improved modularity, e.g. SciPy is not required anymore, but still very useful.
    • Initial implementations of ICA and PCA mapper using functionality provided by MDP. These mappers are more or less untested and should be used with great care.
    • Further improved docstrings of some classes, but still a long way to go.
    • New 'boxcar' mapper, which is the similar to the already present transformWithBoxCar() function, but implemented as a mapper.
    • New SampleGroupMapper that can be used for e.g. block averaging of samples. See new FAQ item.
    • Stripped redundant suffixes from module names, e.g. mvpa.datasets.niftidataset -> mvpa.datasets.nifti
    • mvpa.misc.cmdline variables opt* and opts* were groupped within opt and optss class instances. Also names of the options were changed to match 'dest' of the options. Use tools/refactor.py to quickly fix your custom code.
    • Change all references to PyMVPA website to www.pymvpa.org.
    • Make website stylesheet compatible with sphinx 0.4.
    • Several minor improvements of the compatibility with MacOS.
    • Extended FAQ section of the manual.
    • Bugfix: doubleGammaHRF() ignoring K2 argument.
  • 0.2.2 (Tue, 17 Jun 2008)
    • Extended build instructions: Added section on OpenSUSE.
    • Replaced ugly PYMVPA_LIBSVM environment variable to trigger compiling the LIBSVM wrapper with a proper '--with-libsvm' switch in setup.py. Additionally, setup.py now detects if included LIBSVM has been built and enables LIBSVM wrapper automatically in this case.
    • Added proper Makefiles for LIBSVM copy, with configurable compiler flags.
    • Added 'setup.cfg' to remove the need to manually specify swig-opts (Windows specific configuration is in 'setup.cfg.win').
  • 0.2.1 (Sun, 15 Jun 2008)
    • Several improvements to make building PyMVPA on Windows systems easy (e.g. added dedicated Makefile.win to build a binary installer).
    • Improved and extended documentation for building and installing PyMVPA.
    • Include a minimal copy of the required (patched) LIBSVM library (currently version 2.85.0) for convenience. This copy is automatically compiled and used for the LIBSVM wrapper when PyMVPA built using the Make approach.
  • 0.2.0 (Wed, 29 May 2008)
    • New Splitter class (HalfSplitter) to split into first and second half.
    • New Splitter class (CustomSplitter) to allow for splits with an arbitrary number of datasets per split and the ability to specify the association of samples with any of those datasets (not just the validation set).
    • New sparse multinomial logistic regression (SMLR) classifier and associated sensitivity analyzer.
    • New least angle regression classifier (LARS).
    • New Gaussian process regression classifier (GPR).
    • Initial documentation on extending PyMVPA.
    • Switch to Sphinx for documentation handling.
    • New example comparing the performance of all classifiers on some artificial datasets.
    • New data mapper performing singular value decomposition (SVDMapper) and an example showing its usage.
    • More sophisticated data preprocessing: removal of non-linear trends and other arbitrary confounding regressors.
    • New Harvester class to feed data from arbitrary generators into multiple objects and store results of returned values and arbitrary properties.
    • Added documentation about how to build patched libsvm version with sane debug output.
    • libsvm bindings are not build by default anymore. Instructions on how to reenable them are available in the manual.
    • New wrapper from SVM implementation of the Shogun toolbox.
    • Important bugfix in RFE, which reported incorrect feature ids in some cases.
    • Added ability to compute stats/probabilities for all measures and transfer errors.
  • 0.1.0 (Wed, 20 Feb 2008)
    • First public release.