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        "author": "Laurent El Shafey",
        "author_email": "laurent.el-shafey@idiap.ch",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: GNU General Public License v3 (GPLv3)",
            "Natural Language :: English",
            "Programming Language :: Python",
            "Topic :: Scientific/Engineering :: Artificial Intelligence"
        ],
        "description": "============================\n MLP L-BFGS Trainer for Bob\n============================\n\nThis example demonstrates how to extend Bob by providing a new L-BFGS-based\ntrainer for the multilayer perceptron (MLP) implementation of Bob.\n\nInstallation\n============\n\nFirst, you have to install `bob <http://www.idiap.ch/software/bob>`_ following the instructions\n`there <http://www.idiap.ch/software/bob/docs/releases/last/sphinx/html/Installation.html>`_.\n\n.. note:: \n\n  If you are reading this page through our GitHub portal and not through PyPI,\n  note **the development tip of the package may not be stable** or become\n  unstable in a matter of moments.\n\n  Go to `http://pypi.python.org/pypi/xbob.mlp.lbfgs\n  <http://pypi.python.org/pypi/xbob.mlp.lbfgs>`_ to download the latest\n  stable version of this package.\n\nThere are two options you can follow to get this package installed and \noperational on your computer: you can use automatic installers like `pip\n<http://pypi.python.org/pypi/pip/>`_ (or `easy_install\n<http://pypi.python.org/pypi/setuptools>`_) or manually download, unpack and \nuse `zc.buildout <http://pypi.python.org/pypi/zc.buildout>`_ to create a\nvirtual work environment just for this package. In both cases, the two \ndependences listed above will be automatically downloaded and installed.\n\nUsing an automatic installer\n----------------------------\n\nUsing ``pip`` is the easiest (shell commands are marked with a ``$`` signal)::\n\n  $ pip install xbob.mlp.lbfgs\n\nYou can also do the same with ``easy_install``::\n\n  $ easy_install xbob.mlp.lbfgs\n\nThis will download and install this package plus any other required\ndependencies. It will also verify if the version of Bob you have installed\nis compatible.\n\nThis scheme works well with virtual environments by `virtualenv\n<http://pypi.python.org/pypi/virtualenv>`_ or if you have root access to your\nmachine. Otherwise, we recommend you use the next option.\n\nUsing ``zc.buildout``\n---------------------\n\nDownload the latest version of this package from `PyPI\n<http://pypi.python.org/pypi/xbob.mlp.lbfgs>`_ and unpack it in your\nworking area. The installation of the toolkit itself uses `buildout\n<http://www.buildout.org/>`_. You don't need to understand its inner workings\nto use this package. Here is a recipe to get you started::\n  \n  $ python bootstrap.py \n  $ ./bin/buildout\n\nThese two commands should download and install all non-installed dependencies and \nget you a fully operational test and development environment.\n\n.. note::\n\n  The python shell used in the first line of the previous command set\n  determines the python interpreter that will be used for all scripts developed\n  inside this package. Because this package makes use of `Bob`_, you must make sure that the ``bootstrap.py``\n  script is called with the **same** interpreter used to build Bob, or\n  unexpected problems might occur.\n\n  If Bob is installed by the administrator of your system, it is safe to\n  consider it uses the default python interpreter. In this case, the above 3\n  command lines should work as expected. If you have Bob installed somewhere\n  else on a private directory, edit the file ``buildout.cfg`` **before**\n  running ``./bin/buildout``. Find the section named ``buildout`` and edit or\n  add the line ``prefixes`` to point to the directory where Bob is installed or\n  built. For example::\n\n    [buildout]\n    ...\n    prefixes=/Users/crazyfox/work/bob/build\n\n\nUser Guide\n==========\n\nIt is assumed you have followed the installation instructions for the package\nand got this package installed.\n\nBelow, we provide an example of how to train an MLP using this trainer, from \nthe python universe::\n\n  >>> machine = bob.machine.MLP((n_inputs, n_hidden, n_outputs))\n  >>> # Initialize the machine weights/biases as wished\n  >>> trainer = xbob.mlp.lbfgs.Trainer(1e-6)\n  >>> trainer.initialize(machine)\n  >>> trainer.train(machine, X, labels)",
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