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    "info": {
        "author": "('Maximilian Nickel',)",
        "author_email": "mnick@mit.edu",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 3 - Alpha",
            "Intended Audience :: Developers",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: GNU General Public License v3 (GPLv3)",
            "Operating System :: MacOS",
            "Operating System :: Microsoft :: Windows",
            "Operating System :: POSIX",
            "Operating System :: Unix",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.6",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.3",
            "Topic :: Scientific/Engineering",
            "Topic :: Software Development"
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        "description": "scikit-tensor\n=============\n\nscikit-tensor is a Python module for multilinear algebra and tensor\nfactorizations.\n\nDependencies\n------------\n\nThe required dependencies to build the software are ``Numpy >= 1.3``,\n``SciPy >= 0.7``.\n\nUsage\n-----\n\nExample script to decompose sensory bread data (available from\nhttp://www.models.life.ku.dk/datasets) using CP-ALS\n\n.. code:: python\n\n    import logging\n    from scipy.io.matlab import loadmat\n    from sktensor import dtensor, cp_als\n\n    # Set logging to DEBUG to see CP-ALS information\n    logging.basicConfig(level=logging.DEBUG)\n\n    # Load Matlab data and convert it to dense tensor format\n    mat = loadmat('../data/sensory-bread/brod.mat')\n    T = dtensor(mat['X'])\n\n    # Decompose tensor using CP-ALS\n    P, fit, itr, exectimes = cp_als(T, 3, init='random')\n\nReferences\n----------\n\nIf you use ``scikit-tensor`` in your research, please cite\n\n::\n\n    Maximilian Nickel. scikit-tensor Library (Version 0.1). Available Online, November 2013.\n\nInstall\n-------\n\nThis package uses distutils, which is the default way of installing\npython modules. To install in your home directory, use\n\n::\n\n    python setup.py install --user\n\nTo install for all users on Unix/Linux\n\n::\n\n    python setup.py build\n    sudo python setup.py install\n\nTo install in development mode\n\n::\n\n    python setup.py develop\n\nContributing & Development\n--------------------------\n\nscikit-tensor is still an extremely young project, and I'm happy for any\ncontributions (patches, code, bugfixes, *documentation*, whatever) to\nget it to a stable and useful point. Feel free to get in touch with me\nvia email (mnick at AT mit DOT edu) or directly via github.\n\nDevelopment is synchronized via git. To clone this repository, run\n\n::\n\n    git clone git://github.com/scikit-learn/scikit-learn.git\n\nAuthors\n-------\n\nMaximilian Nickel\n\n-  http://twitter.com/mnick\n-  http://github.com/mnick\n\nLicense\n-------\n\nscikit-tensor is licensed under the GPLv3\nhttp://www.gnu.org/licenses/gpl-3.0.txt",
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