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"info": {
"author": "Laurent El Shafey",
"author_email": "laurent.el-shafey@idiap.ch",
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"Development Status :: 5 - Production/Stable",
"Environment :: Console",
"Intended Audience :: Developers",
"Intended Audience :: Education",
"Intended Audience :: Science/Research",
"License :: OSI Approved :: GNU General Public License v3 (GPLv3)",
"Natural Language :: English",
"Programming Language :: Python",
"Programming Language :: Python :: 3",
"Topic :: Database :: Front-Ends",
"Topic :: Scientific/Engineering :: Artificial Intelligence"
],
"description": "================\n MNIST Database\n================\n\nThe MNIST database is a database of handwritten digits, which consists of a\ntraining set of 60,000 examples, and a test set of 10,000 examples. It was\nmade available by Yann Le Cun and Corinna Cortes (`MNIST database\n`_). The data was originally extracted\nfrom a larger set made available by `NIST `_, before\nbeing size-normalized and centered in a fixed-size image (28x28 pixels).\n\nThe actual raw data for the database should be downloaded from the `original\nwebsite `_. This package only contains\nthe `Bob `_ accessor methods to use this\ndatabase directly from python.\n\nYou would normally not install this package unless you are maintaining it. What\nyou would do instead is to tie it in at the package you need to **use** it.\nThere are a few ways to achieve this:\n\n1. You can add this package as a requirement at the ``setup.py`` for your own\n `satellite package\n `_\n or to your Buildout ``.cfg`` file, if you prefer it that way. With this\n method, this package gets automatically downloaded and installed on your\n working environment, or\n\n2. You can manually download and install this package using commands like\n ``easy_install`` or ``pip``.\n\nThe package is available in two different distribution formats:\n\n1. You can download it from `PyPI `_, or\n\n2. You can download it in its source form from `its git repository\n `_.\n\nThe database raw files must be installed somewhere in your environment.\n\nYou can mix and match points 1/2 above based on your requirements. Here\nare some examples:\n\nModify your setup.py and download from PyPI\n===========================================\n\nThat is the easiest. Edit your ``setup.py`` in your satellite package and add\nthe following entry in the ``install_requires`` section (note: ``...`` means\n`whatever extra stuff you may have in-between`, don't put that on your\nscript)::\n\n install_requires=[\n ...\n \"xbob.db.mnist\",\n ],\n\nProceed normally with your ``bootstrap/buildout`` steps and you should be all\nset. That means you can now import the namespace ``xbob.db.mnist`` into your scripts.\n\nModify your buildout.cfg and download from git\n==============================================\n\nYou will need to add a dependence to `mr.developer\n`_ to be able to install from our\ngit repositories. Your ``buildout.cfg`` file should contain the following\nlines::\n\n [buildout]\n ...\n extensions = mr.developer\n auto-checkout = *\n eggs = bob\n ...\n xbob.db.mnist\n\n [sources]\n xbob.db.mnist = git https://github.com/bioidiap/xbob.db.mnist.git\n ...\n\n\nHow to use this database API\n============================\n\nAfter launching the python interpreter (assuming that the environment is properly set up),\nyou could get the training set as follows::\n\n >>> import xbob.db.mnist\n >>> db = xbob.db.mnist.Database('PATH_TO_DATA_FROM_YANN_LECUN_WEBSITE') # 4 binary .gz compressed files\n >>> images, labels = db.data(groups='train', labels=[0,1,2,3,4,5,6,7,8,9])\n\nIn this case, this should return two NumPy arrays:\n\n1. `images` contain the raw data (60,000 samples of dimension 784 [28x28 pixels images])\n\n2. `labels` are the corresponding classes (digits 0 to 9) for each of the 60,000 samples\n\n\nIf you don't have the data installed on your machine, you can also use the following\nset of commands that will:\n\n1. first look for the database in the xbob/db/mnist/ subdirectory and use it if is available\n\n2. automatically download it from Yann Lecun's website into a temporary folder that will\n be erased when the destructor of the xbob.db.mnist database is called.\n\n3. automatically download it into the provided directory that will **not** be deleted.\n\n::\n\n >>> import xbob.db.mnist\n >>> db = xbob.db.mnist.Database() # Check for the data files locally, and download them if required\n >>> images, labels = db.data(groups='train', labels=[0,1,2,3,4,5,6,7,8,9])\n >>> del db # delete the temporary downloaded files if any\n\nor\n\n::\n\n >>> db = xbob.db.mnist.Database(\"Directory\") # Persistently downloads files into the folder \"Directory\"\n >>> images, labels = db.data(groups='train', labels=[0,1,2,3,4,5,6,7,8,9])\n >>> del db # The download directory stays",
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