New README and documentation strategy.

.travis.yml

@@ -6,6 +6,7 @@ matrix:
     env:
     - secure: gX4Yr/32b9tOVFiAxjzFsbIm1VWiB6WJ1rXOd4hCd6ncBaXw83e4B/jaP1Ci2uKRi6PYO9y7AHj8y64OcTeS2Uwr+5QECRSRgf6+AKCde4VgKoh5wUpVOp4QLaZlJLNM6L/EwGWih5K2yoM/17cRLyGQC/x6QID/ZfEjwtZni2M=
     - secure: kq32QuV+vu0Qaqo1wn4UvUNAglNF4HGJv96XNwf263G+uZekCMpx6DJdiVJouxoYRcOsgkoJgbc1rxAFGUZ7PZ4gfP6srQ9i3ZT7H3zghVBr8ULwmi9+TR58W/P+KBZl/lLVSsdKeMVfT2hJ9XpF1P6G8GM/3UkTP3Rp0t8SIug=
+    - BOB_DOCUMENTATION_SERVER=https://www.idiap.ch/software/bob/docs/latest/bioidiap/%s/master
   - python: 3.2
     env:
     - NUMPYSPEC===1.7.1
@@ -15,7 +16,7 @@ matrix:
 before_install:
 - sudo add-apt-repository -y ppa:biometrics/bob
 - sudo apt-get update -qq
-- sudo apt-get install -qq --force-yes libboost-all-dev libblitz1-dev libhdf5-serial-dev libatlas-dev libatlas-base-dev liblapack-dev
+- sudo apt-get install -qq --force-yes libboost-all-dev libblitz1-dev libhdf5-serial-dev libatlas-dev libatlas-base-dev liblapack-dev texlive-latex-recommended texlive-latex-extra texlive-fonts-recommended
 - if [ -n "${NUMPYSPEC}" ]; then sudo apt-get install -qq  gfortran; fi
 - if [ -n "${NUMPYSPEC}" ]; then pip install --upgrade pip setuptools; fi
 - if [ -n "${NUMPYSPEC}" ]; then pip install --find-links http://wheels.astropy.org/ --find-links http://wheels2.astropy.org/ --use-wheel numpy$NUMPYSPEC; fi

README.rst

@@ -2,78 +2,36 @@
 .. Andre Anjos <andre.anjos@idiap.ch>
 .. Thu 24 Apr 17:24:10 2014 CEST
 
-.. image:: https://travis-ci.org/bioidiap/bob.learn.mlp.svg?branch=master
-   :target: https://travis-ci.org/bioidiap/bob.learn.mlp
+.. image:: http://img.shields.io/badge/docs-stable-yellow.png
+   :target: http://pythonhosted.org/bob.learn.mlp/index.html
 .. image:: http://img.shields.io/badge/docs-latest-orange.png
    :target: https://www.idiap.ch/software/bob/docs/latest/bioidiap/bob.learn.mlp/master/index.html
+.. image:: https://travis-ci.org/bioidiap/bob.learn.mlp.svg?branch=master
+   :target: https://travis-ci.org/bioidiap/bob.learn.mlp
 .. image:: https://coveralls.io/repos/bioidiap/bob.learn.mlp/badge.png
    :target: https://coveralls.io/r/bioidiap/bob.learn.mlp
-.. image:: http://img.shields.io/github/tag/bioidiap/bob.learn.mlp.png
-   :target: https://github.com/bioidiap/bob.learn.mlp
+.. image:: https://img.shields.io/badge/github-master-0000c0.png
+   :target: https://github.com/bioidiap/bob.learn.mlp/tree/master
 .. image:: http://img.shields.io/pypi/v/bob.learn.mlp.png
    :target: https://pypi.python.org/pypi/bob.learn.mlp
 .. image:: http://img.shields.io/pypi/dm/bob.learn.mlp.png
    :target: https://pypi.python.org/pypi/bob.learn.mlp
 
-===============================================================
- Python bindings for Bob's Multi-Layer Perceptron and Trainers
-===============================================================
+============================================
+ Multi-Layer Perceptron and Trainers in Bob
+============================================
 
 This package contains a set of Pythonic bindings for Bob's MLP and Trainers.
 
 Installation
 ------------
-
-Install it through normal means, via PyPI or use ``zc.buildout`` to bootstrap
-the package and run test units.
+To install this package -- alone or together with other `Packages of Bob <https://github.com/idiap/bob/wiki/Packages>`_ -- please read the `Installation Instructions <https://github.com/idiap/bob/wiki/Installation>`_.
+For Bob_ to be able to work properly, some dependent packages are required to be installed.
+Please make sure that you have read the `Dependencies <https://github.com/idiap/bob/wiki/Dependencies>`_ for your operating system.
 
 Documentation
 -------------
+For further documentation on this package, please read the `Stable Version <http://pythonhosted.org/bob.learn.mlp/index.html>`_ or the `Latest Version <https://www.idiap.ch/software/bob/docs/latest/bioidiap/bob.learn.mlp/master/index.html>`_ of the documentation.
+For a list of tutorials on this or the other packages ob Bob_, or information on submitting issues, asking questions and starting discussions, please visit its website.
 
-The latest version of the documentation can be found `here <https://www.idiap.ch/software/bob/docs/latest/bioidiap/bob.learn.mlp/master/index.html>`_.
-
-Otherwise, you can generate the documentation for this package yourself, after installation, using Sphinx::
-
-  $ sphinx-build -b html doc sphinx
-
-This shall place in the directory ``sphinx``, the current version for the
-documentation of the package.
-
-Testing
--------
-
-You can run a set of tests using the nose test runner::
-
-  $ nosetests -sv
-
-.. warning::
-
-   If Bob <= 1.2.1 is installed on your python path, nose will automatically
-   load the old version of the insulate plugin available in Bob, which will
-   trigger the loading of incompatible shared libraries (from Bob itself), in
-   to your working binary. This will cause a stack corruption. Either remove
-   the centrally installed version of Bob, or build your own version of Python
-   in which Bob <= 1.2.1 is not installed.
-
-You can run our documentation tests using sphinx itself::
-
-  $ sphinx-build -b doctest doc sphinx
-
-You can test overall test coverage with::
-
-  $ nosetests --with-coverage --cover-package=bob.learn.mlp
-
-The ``coverage`` egg must be installed for this to work properly.
-
-Development
------------
-
-To develop this package, install using ``zc.buildout``, using the buildout
-configuration found on the root of the package::
-
-  $ python bootstrap.py
-  ...
-  $ ./bin/buildout
-
-Tweak the options in ``buildout.cfg`` to disable/enable verbosity and debug
-builds.
+.. _bob: https://www.idiap.ch/software/bob

bob/learn/mlp/test_utils.py

@@ -33,12 +33,12 @@ class Machine:
     hidden_activation
       The activation function to use for the hidden neurons of the network.
       Should be one of the classes derived from
-      :py:class:`bob.machine.Activation`.
+      :py:class:`bob.learn.activation.Activation`.
 
     output_activation
       The activation function to use for the output neurons of the network.
       Should be one of the classes derived from
-      :py:class:`bob.machine.Activation`.
+      :py:class:`bob.learn.activation.Activation`.
     """
 
     if bias is None:
@@ -112,7 +112,7 @@ class TrainableMachine(Machine):
 
     b
       This is the error back-propagated through the last neuron by any of the
-      available :py:class:`bob.trainer.Cost` functors. Every row in b matches
+      available :py:class:`bob.learn.mlp.Cost` functors. Every row in b matches
       one example.
 
     self.d

bob/learn/mlp/trainer.cpp

@@ -220,7 +220,7 @@ PyDoc_STRVAR(s_cost_object_str, "cost_object");
 PyDoc_STRVAR(s_cost_object_doc,
 "An object, derived from :py:class:`bob.learn.mlp.Cost` (e.g.\n\
 :py:class:`bob.learn.mlp.SquareError` or \n\
-:py:class:`bob.trainer.CrossEntropyLoss`), that is used to evaluate\n\
+:py:class:`bob.learn.mlp.CrossEntropyLoss`), that is used to evaluate\n\
 the cost (a.k.a. *loss*) and the derivatives given the input, the\n\
 target and the MLP structure.");
 

buildout.cfg

@@ -19,6 +19,7 @@ develop = src/bob.extension
 ; options for bob.buildout extension
 debug = true
 verbose = true
+newest = false
 
 [sources]
 bob.extension = git https://github.com/bioidiap/bob.extension

doc/conf.py

@@ -249,36 +249,11 @@ autoclass_content = 'both'
 autodoc_member_order = 'bysource'
 autodoc_default_flags = ['members', 'undoc-members', 'inherited-members', 'show-inheritance']
 
-def smaller_than(v1, v2):
-  """Compares scipy/numpy version numbers"""
-
-  c1 = v1.split('.')
-  c2 = v2.split('.')[:len(c1)] #clip to the compared version
-  for i, k in enumerate(c2):
-    n1 = c1[i]
-    n2 = c2[i]
-    try:
-      n1 = int(n1)
-      n2 = int(n2)
-    except ValueError:
-      n1 = str(n1)
-      n2 = str(n2)
-    if n1 > n2: return False
-  return True
-
-# Some name mangling to find the correct sphinx manuals for some packages
-numpy_version = __import__('numpy').version.version
-if smaller_than(numpy_version, '1.5.z'):
-  numpy_version = '.'.join(numpy_version.split('.')[:-1]) + '.x'
-else:
-  numpy_version = '.'.join(numpy_version.split('.')[:-1]) + '.0'
-numpy_manual = 'http://docs.scipy.org/doc/numpy-%s/' % numpy_version
-
 # For inter-documentation mapping:
-intersphinx_mapping = {
-  'http://docs.python.org/%d.%d/' % sys.version_info[:2]: None,
-  numpy_manual: None,
-  }
+from bob.extension.utils import link_documentation
+# the documentation also links to bob.learn.linear
+intersphinx_mapping = link_documentation(['python', 'numpy', 'bob.learn.linear'])
+
 
 def setup(app):
   pass

doc/guide.rst

@@ -100,7 +100,7 @@ this example:
 
 Once the network weights and biases are set, we can feed forward an example
 through this machine. This is done using the ``()`` operator, like for a
-:py:class:`bob.learn.Linear.Machine`:
+:py:class:`bob.learn.linear.Machine`:
 
 .. doctest::
 
@@ -120,7 +120,7 @@ available MLP trainers in two different 2D `NumPy`_ arrays, one for the input
    >>> t0 = numpy.array([[.0]]) # target
 
 The class used to train a MLP [1]_ with backpropagation [2]_ is
-:py:class:`bob.learn.MLP.BackProp`. An example is shown below.
+:py:class:`bob.learn.mlp.BackProp`. An example is shown below.
 
 
 .. doctest::

setup.py

@@ -21,7 +21,7 @@ setup(
 
     name='bob.learn.mlp',
     version=version,
-    description='Bindings for bob.machine\'s Multi-layer Perceptron and Trainers',
+    description='Bob\'s Multi-layer Perceptron and Trainers',
     url='http://github.com/bioidiap/bob.learn.mlp',
     license='BSD',
     author='Andre Anjos',
@@ -39,7 +39,7 @@ setup(
     namespace_packages=[
       "bob",
       "bob.learn",
-      ],
+    ],
 
     ext_modules = [
       Extension("bob.learn.mlp.version",
@@ -97,13 +97,14 @@ setup(
     },
 
     classifiers = [
-      'Development Status :: 3 - Alpha',
+      'Framework :: Bob',
+      'Development Status :: 4 - Beta',
       'Intended Audience :: Developers',
       'License :: OSI Approved :: BSD License',
       'Natural Language :: English',
       'Programming Language :: Python',
       'Programming Language :: Python :: 3',
       'Topic :: Software Development :: Libraries :: Python Modules',
-      ],
+    ],
 
-    )
+)