.. currentmodule:: scikits.learn

0.5
===

.. _changes_0_5:

Changelog
---------

New classes
~~~~~~~~~~~~

    - Support for sparse matrices in some classifiers of modules
      ``svm`` and ``glm`` (see :class:`svm.sparse.SVC`,
      :class:`svm.sparse.SVR`, :class:`svm.sparse.LinearSVC`,
      :class:`glm.sparse.Lasso`, :class:`glm.sparse.ElasticNet`)

    - New :class:`pipeline.Pipeline` object to compose different estimators.

    - Recursive Feature Elimination routines in module
      :ref:`feature_selection_doc`.

    - Addition of various classes capable of cross validation in the
      glm module (:class:`glm.LassoCV`, :class:`glm.ElasticNetCV`,
      etc.).

    - New, more efficient LARS algorithm implementation. The Lasso
      variant of the algorithm is also implemented. See
      :class:`glm.lars_path`, :class:`glm.LARS` and
      :class:`glm.LassoLARS`.

    - New Hidden Markov Models module (see classes
      :class:`hmm.GaussianHMM`, :class:`hmm.MultinomialHMM`,
      :class:`hmm.GMMHMM`)

    - New module feature_extraction (see :ref:`class reference
      <feature_extraction_ref>`)

    - New FastICA algorithm in module scikits.learn.fastica


Documentation
~~~~~~~~~~~~~

    - Improved documentation for many modules, now separating
      narrative documentation from the class reference. As an example,
      see `documentation for the SVM module
      <http://scikit-learn.sourceforge.net/modules/svm.html>`_ and the
      complete `class reference
      <http://scikit-learn.sourceforge.net/modules/classes.html>`_.

Fixes
~~~~~

    - API changes: adhere variable names to PEP-8, give more
      meaningful names.

    - Fixes for svm module to run on a shared memory context
      (multiprocessing).

    - It is again possible to generate latex (and thus PDF) from the
      sphinx docs.

Examples
~~~~~~~~

    - new examples using some of the mlcomp datasets:
      :ref:`example_mlcomp_sparse_document_classification.py`,
      :ref:`example_mlcomp_document_classification.py`

    - Many more examaples. `See here
      <http://scikit-learn.sourceforge.net/auto_examples/index.html>`_
      the full list of examples.


External dependencies
~~~~~~~~~~~~~~~~~~~~~

    - Joblib is now a dependencie of this package, although it is
      shipped with (scikits.learn.externals.joblib).

Removed modules
~~~~~~~~~~~~~~~

    - Module ann (Artificial Neural Networks) has been removed from
      the distribution. Users wanting this sort of algorithms should
      take a look into pybrain.

Misc
~~~~

    - New sphinx theme for the web page.


Authors
-------

The following is a list of authors for this release, preceeded by
number of commits:

     * 262  Fabian Pedregosa
     * 240  Gael Varoquaux
     * 149  Alexandre Gramfort
     * 116  Olivier Grisel
     *  40  Vincent Michel
     *  38  Ron Weiss
     *  23  Matthieu Perrot
     *  10  Bertrand Thirion
     *   7  Yaroslav Halchenko
     *   9  VirgileFritsch
     *   6  Edouard Duchesnay
     *   4  Mathieu Blondel
     *   1  Ariel Rokem
     *   1  Matthieu Brucher

0.4
===

Changelog
---------

Major changes in this release include:

    - Coordinate Descent algorithm (Lasso, ElasticNet) refactoring & 
      speed improvements (roughly 100x times faster).

    - Coordinate Descent Refactoring (and bug fixing) for consistency
      with R's package GLMNET.

    - New metrics module.

    - New GMM module contributed by Ron Weiss.

    - Implementation of the LARS algorithm (without Lasso variant for now).

    - feature_selection module redesign.

    - Migration to GIT as content management system.

    - Removal of obsolete attrselect module.

    - Rename of private compiled extensions (aded underscore).

    - Removal of legacy unmaintained code.

    - Documentation improvements (both docstring and rst).

    - Improvement of the build system to (optionally) link with MKL. 
 Also, provide a lite BLAS implementation in case no system-wide BLAS is 
 found.

    - Lots of new examples.

    - Many, many bug fixes ...


Authors
-------

The committer list for this release is the following (preceded by number 
of commits):

    * 143  Fabian Pedregosa
    * 35  Alexandre Gramfort
    * 34  Olivier Grisel
    * 11  Gael Varoquaux
    *  5  Yaroslav Halchenko
    *  2  Vincent Michel
    *  1  Chris Filo Gorgolewski