<commit_before>
..  
    We are putting the title as a raw HTML so that it doesn't appear in
    the contents

.. raw:: html

    <h1>scikits.learn: machine learning in Python</h1>

.. only:: html

    .. |banner1| image:: auto_examples/cluster/images/plot_affinity_propagation.png
       :height: 150
       :target: auto_examples/cluster/plot_affinity_propagation.html


    .. |banner2| image:: auto_examples/glm/images/plot_lasso_lars.png
       :height: 150
       :target: auto_examples/glm/plot_lasso_lars.html

    .. |banner3| image:: auto_examples/svm/images/plot_oneclass.png
       :height: 150
       :target: auto_examples/svm/plot_oneclass.html

    .. |banner4| image:: auto_examples/cluster/images/plot_lena_segmentation.png
       :height: 150
       :target: auto_examples/cluster/plot_lena_segmentation.html

    .. |center-div| raw:: html

        <div style="text-align: center; margin: 0px 0 -5px 0;">

    .. |end-div| raw:: html

        </div>


    |center-div| |banner1| |banner2| |banner3| |banner4| |end-div| 


.. topic:: Easy-to-use and general-purpose machine learning in Python

    ``scikits.learn`` is a Python module integrating classic machine
    learning algorithms in the tightly-knit world of scientific Python
    packages (`numpy <http://www.scipy.org>`_, `scipy
    <http://www.scipy.org>`_, `matplotlib
    <http://matplotlib.sourceforge.net/>`_).
    
    It aims to provide simple and efficient solutions to learning
    problems that are accessible to everybody and reusable in various
    contexts: **machine-learning as a versatile tool for science and
    engineering**.
    


:Features:
  * **Solid**: :ref:`supervised-learning`: classification, regression

  * **Work in progress**: :ref:`unsupervised-learning`: :ref:`clustering`, 
    :ref:`gmm`, manifold learning, :ref:`ICA <ICA>`

  * **Planed**: Gaussian graphical models, matrix factorization

:License:
  Open source, commercially usable: **BSD license** (3 clause)


.. only:: html

    .. raw:: html

       <div class="example_digits">

    :ref:`A simple Example: recognizing hand-written digits <example_plot_digits_classification.py>` ::

        import pylab as pl

        from scikits.learn import datasets, svm
        digits = datasets.load_digits()
        for index, (image, label) in enumerate(zip(digits.images, digits.target)[:4]):
            pl.subplot(2, 4, index+1)
            pl.imshow(image, cmap=pl.cm.gray_r)
            pl.title('Training: %i' % label)

        n_samples = len(digits.images)
        data = digits.images.reshape((n_samples, -1))

        classifier = svm.SVC()
        classifier.fit(data[:n_samples/2], digits.target[:n_samples/2])

        for index, image in enumerate(digits.images[n_samples/2:n_samples/2+4]):
            pl.subplot(2, 4, index+5)
            pl.imshow(image, cmap=pl.cm.gray_r)
            pl.title('Prediction: %i' % classifier.predict(image.ravel()))

    .. image:: images/plot_digits_classification.png
       :height: 140
       :target: auto_examples/plot_digits_classification.html

    .. raw:: html

        </div>


.. warning:: 

   This documentation is for the development version,
   documentation for the stable version can be found `here
   <http://scikit-learn.sourceforge.net/old_doc/>`__

.. include:: big_toc_css.rst

First steps
======================

.. toctree::
   :maxdepth: 2

   install
   tutorial

User guide
==========

.. toctree::
   :maxdepth: 2

   contents

Example gallery
=================

.. toctree::
   :maxdepth: 2

   auto_examples/index


Developement
============
.. toctree::
   :maxdepth: 2

   developers/index
   performance
   about<commit_msg>DOC: spelling<commit_after>
..  
    We are putting the title as a raw HTML so that it doesn't appear in
    the contents

.. raw:: html

    <h1>scikits.learn: machine learning in Python</h1>

.. only:: html

    .. |banner1| image:: auto_examples/cluster/images/plot_affinity_propagation.png
       :height: 150
       :target: auto_examples/cluster/plot_affinity_propagation.html


    .. |banner2| image:: auto_examples/glm/images/plot_lasso_lars.png
       :height: 150
       :target: auto_examples/glm/plot_lasso_lars.html

    .. |banner3| image:: auto_examples/svm/images/plot_oneclass.png
       :height: 150
       :target: auto_examples/svm/plot_oneclass.html

    .. |banner4| image:: auto_examples/cluster/images/plot_lena_segmentation.png
       :height: 150
       :target: auto_examples/cluster/plot_lena_segmentation.html

    .. |center-div| raw:: html

        <div style="text-align: center; margin: 0px 0 -5px 0;">

    .. |end-div| raw:: html

        </div>


    |center-div| |banner1| |banner2| |banner3| |banner4| |end-div| 


.. topic:: Easy-to-use and general-purpose machine learning in Python

    ``scikits.learn`` is a Python module integrating classic machine
    learning algorithms in the tightly-knit world of scientific Python
    packages (`numpy <http://www.scipy.org>`_, `scipy
    <http://www.scipy.org>`_, `matplotlib
    <http://matplotlib.sourceforge.net/>`_).
    
    It aims to provide simple and efficient solutions to learning
    problems that are accessible to everybody and reusable in various
    contexts: **machine-learning as a versatile tool for science and
    engineering**.
    


:Features:
  * **Solid**: :ref:`supervised-learning`: classification, regression

  * **Work in progress**: :ref:`unsupervised-learning`: :ref:`clustering`, 
    :ref:`gmm`, manifold learning, :ref:`ICA <ICA>`

  * **Planed**: Gaussian graphical models, matrix factorization

:License:
  Open source, commercially usable: **BSD license** (3 clause)


.. only:: html

    .. raw:: html

       <div class="example_digits">

    :ref:`A simple Example: recognizing hand-written digits <example_plot_digits_classification.py>` ::

        import pylab as pl

        from scikits.learn import datasets, svm
        digits = datasets.load_digits()
        for index, (image, label) in enumerate(zip(digits.images, digits.target)[:4]):
            pl.subplot(2, 4, index+1)
            pl.imshow(image, cmap=pl.cm.gray_r)
            pl.title('Training: %i' % label)

        n_samples = len(digits.images)
        data = digits.images.reshape((n_samples, -1))

        classifier = svm.SVC()
        classifier.fit(data[:n_samples/2], digits.target[:n_samples/2])

        for index, image in enumerate(digits.images[n_samples/2:n_samples/2+4]):
            pl.subplot(2, 4, index+5)
            pl.imshow(image, cmap=pl.cm.gray_r)
            pl.title('Prediction: %i' % classifier.predict(image.ravel()))

    .. image:: images/plot_digits_classification.png
       :height: 140
       :target: auto_examples/plot_digits_classification.html

    .. raw:: html

        </div>


.. warning:: 

   This documentation is for the development version,
   documentation for the stable version can be found `here
   <http://scikit-learn.sourceforge.net/old_doc/>`__

.. include:: big_toc_css.rst

First steps
======================

.. toctree::
   :maxdepth: 2

   install
   tutorial

User guide
==========

.. toctree::
   :maxdepth: 2

   contents

Example gallery
=================

.. toctree::
   :maxdepth: 2

   auto_examples/index


Development
===========
.. toctree::
   :maxdepth: 2

   developers/index
   performance
   about
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