Update the documentation

doc/bob.rst

-bob package
-===========
-
-Subpackages
------------
-
-.. toctree::
-
-    bob.fusion
-
-Module contents
----------------
-
-.. automodule:: bob
-    :members:
-    :undoc-members:
-    :show-inheritance:

doc/conf.py

@@ -247,6 +247,8 @@ if os.path.exists(sphinx_requirements):
 else:
   intersphinx_mapping = link_documentation()
 
+# add scikit-learn intersphinx mapping
+intersphinx_mapping['http://scikit-learn.org/stable/'] = None
 
 # We want to remove all private (i.e. _. or __.__) members
 # that are not in the list of accepted functions

doc/guide.rst

 .. vim: set fileencoding=utf-8 :
 .. author: Amir Mohammadi <amir.mohammadi@idiap.ch>
 
-.. _bob.fusion.base.fusion:
+===========================================================
+ Running Score Fusion in Biometric Recognition Experiments
+===========================================================
 
-
-==========================================================
-Running Score Fusion in Biometric Recognition Experiments
-==========================================================
-
-Each score fusion experiment requires at least a classifier.
-The ``bob.fusion.base`` package itself implements three such classifiers: ``MLP``, ``LLR`` and ``WeightedSum``.
-You can also use any class as a classifier that implements a ``fit(X[, y])`` and a ``decision_function(X)`` method.
-An example is `sklearn.linear_model.LogisticRegression <http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html>`_.
-
-You can also use pre-processors to pre-process your data. The pre-processor class should implement a ``fit_transform(X[, y])`` and a ``transform(X[, y, copy])`` methods. An example is `sklearn.preprocessing.StandardScaler <http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html>`_.
-
-Using ``sklearn`` classifiers and pre-processors you can implement different fusion algorithms. Please take a look at some examples in ``bob.fusion.base.config.algorithm`` to see how it is done.
-
-There are two scripts available in the package as of now and they work for verification scenarios.
-Spoofing and Anti-spoofing scenarios are not considered yet **in the scripts** however the Python API allows for easy extension.
-
-Also take a look at the scripts ``bob_fuse.py`` and ``bob_fusion_decision_boundary.py`` and use them for your actual fusion experiments.
+.. todo:: write user guide.
 
 .. include:: links.rst

doc/index.rst

@@ -7,40 +7,17 @@
  Score Fusion in Biometric Recognition Experiments
 ===================================================
 
-The ``bob.fusion.base`` package provides open source tools to run comparable and reproducible score fusion in biometric recognition experiments.
+This package provides open source tools to run comparable and reproducible score
+fusion in biometric recognition experiments. It is written to be tightly
+integrated with bob_ and scikit-learn_. However you do not neccessairly need
+scikit-learn_ to use this package.
 
-It is written to be tightly integrated with ``scikit-learn`` however you do not need to use it neccessairly.
-
-
-===========
-Users Guide
-===========
 
 .. toctree::
    :maxdepth: 2
 
    guide
+   py_api
 
-================
-Reference Manual
-================
-
-.. toctree::
-   :maxdepth: 2
-
-* :ref:`modindex`
-
-
-==========
-References
-==========
-
-
-==================
-Indices and tables
-==================
-
-* :ref:`genindex`
-* :ref:`search`
 
 .. include:: links.rst

doc/links.rst

-.. vim: set fileencoding=utf-8 :
-.. author: Manuel Günther <manuel.guenther@idiap.ch>
-.. date: Thu Sep 20 11:58:57 CEST 2012
-
 .. This file contains all links we use for documentation in a centralized place
 
 .. _idiap: http://www.idiap.ch
-.. _github: http://www.github.com/idiap
 .. _bob: http://www.idiap.ch/software/bob
-.. _github bug reporting system: http://github.com/bioidiap/bob.bio.base/issues
-.. _idiap at github: http://www.github.com/bioidiap
-.. _at&t database: http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html
-.. _bob's github page: http://idiap.github.com/bob
-.. _gridtk: http://github.com/idiap/gridtk
-.. _buildout: http://www.buildout.org
-.. _setuptools: http://trac.edgewall.org/wiki/setuptools
-.. _nist: http://www.nist.gov/itl/iad/ig/focs.cfm
 .. _pypi: http://pypi.python.org
-.. _sge: http://wiki.idiap.ch/linux/SunGridEngine
-.. _csu face recognition resources: http://www.cs.colostate.edu/facerec
-.. _xfacereclib.extension.csu: http://pypi.python.org/pypi/xfacereclib.extension.CSU
-.. _virtualbox: https://www.virtualbox.org
-.. _hdf5: http://www.hdfgroup.org/HDF5
+.. _mailing list: https://www.idiap.ch/software/bob/discuss
+.. _scikit-learn: http://scikit-learn.org

doc/modules.rst

-bob.fusion.base
-===============
-
-.. toctree::
-   :maxdepth: 4
-
-   bob

doc/py_api.rst

+
+==========
+Python API
+==========
+
+This section includes information for using the Python API of
+``bob.fusion.base``.
+
+=================
+Fusion Algorithms
+=================
+
+.. automodule:: bob.fusion.base.algorithm
+   :members:
+   :undoc-members:
+
+
+==============
+Fusion Scripts
+==============
+
+.. automodule:: bob.fusion.base.script
+   :members:
+   :undoc-members: