Welcome
==========================================

.. include:: colors.rst

.. topic:: About

    **MLZ**, "\ **M**\achine \ **L**\earning and photo-\ **Z**\"  is a parallel python framework that
    computes fast and robust photometric redshift PDFs using Machine Learning algorithms. In particular,
    it uses a supervised technique with prediction trees and random forest through :ref:`TPZ <tpz2>` or
    a unsupervised methods with self organizing maps and random atlas
    through :ref:`SOMz <somz2>`. It can be easily extended to other regression or classification problems.
    We recently have added an additional feature that allows high compressed representation of the photo-z
    PDFs using :ref:`sparse representation <sparse2>`. This allow to efficiently store and handle a large number
    of PDF from different techniques


.. _refers:
References
...........
    These are the references related to this framework where detailed information about these methods can be found.

    * Carrasco Kind, M., & Brunner, R. J., 2013  :blueit:`"TPZ : Photometric redshift PDFs and ancillary information by using prediction trees and random forests"`, MNRAS, 432, 1483 (`Link <http://adsabs.harvard.edu/abs/2013MNRAS.432.1483C>`_)

    * Carrasco Kind, M., & Brunner, R. J., 2014, :blueit:`"SOMz : photometric redshift PDFs with self organizing maps and random atlas"` , MNRAS, 438, 3409 (`Link <http://adsabs.harvard.edu/abs/2014MNRAS.438.3409C>`_)

    * Carrasco Kind, M., & Brunner, R. J., 2014, :blueit:`"Exhausting the Information: Novel Bayesian Combination of Photometric Redshift PDFs"`, MNRAS submitted (`Link <http://adsabs.harvard.edu/abs/2014arXiv1403.0044C>`_)

    * Carrasco Kind, M., & Brunner, R. J., 2014, :blueit:`"Sparse Representation of Photometric Redshift PDFs: Preparing for Petascale Astronomy"`, MNRAS in press. (`Link <http://adsabs.harvard.edu/abs/2014arXiv1404.6442C>`_)

Contact
........

`Here <https://sites.google.com/site/mgckind/>`_ you can find my contact information for questions or comments.


Now on GitHub
.............

We have uploaded MLZ to `GitHub <https://github.com/mgckind/MLZ>`_

Contents
........

    This is a brief documentation of MLZ and the routines included

.. toctree::
   :maxdepth: 2

   req
   install
   ml_codes
   others
   run_mlz
   run
   sparse






Indices and tables
..................

* :ref:`genindex`
* :ref:`modindex`
* :ref:`search`

