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        "author": "Jose P Valdes Herrera",
        "author_email": "jpvaldesherrera@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 3 - Alpha",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: BSD License",
            "Programming Language :: Python",
            "Programming Language :: Python :: 3",
            "Topic :: Scientific/Engineering",
            "Topic :: Software Development"
        ],
        "description": "# BrainOwl\n\nThis is a classifier tuned for neuroimaging. In particular for task-related\nfMRI. It is meant to be used with the OWL norm (also called Ordered $l_1$ norm)\nand uses a solver based on SpaRSA.\n\nThe OWL norm should identify features relevant for the learning problem, even if\nthey are correlated. Weight maps based on OWL tend to be sparse, but not so\nsparse like the solutions from LASSO, for example.\n\n# Install\n\nInstall using `pip` (be sure you have python3>=3.5):\n\n``` {.bash}\npip install brainowl\n```\n\nAnd done.\n\nIf you want to have the source code, you can clone the repository using `git`:\n\n``` {.bash}\ngit clone https://github.com/jpvaldes/brainowl.git\n```\n\nand then install it:\n\n``` {.bash}\ncd brainowl\npip install -e .\n```\n\n# Usage example\n\nThe included Jupyter notebook contains an example usage of the BrainOwl\nclassifier showing how to decode two categories of the classic neuroimaging\nHaxby dataset.\n\nThe dataset will be downloaded automatically if it is not found.\n\n# Acknowledgments\n\nThe [scikit-learn](https://scikit-learn.org) library for making it easier to\ndevelop new ideas, the [pyowl](https://https://github.com/vene/pyowl)\nimplementation, and the [nilearn](https://nilearn.github.io) project (in\nparticular, the SpaceNet learners).\n\nThis project contains code from [pyowl](https://https://github.com/vene/pyowl).\n\n# References\n\n    X Zeng, M A T Figueiredo, The Ordered Weighted $l_1$ Norm:\n    Atomic Formulation, Projections, and Algorithms.\n\n    J. Bogdan, E. Berg, W. Su, and E. Candes, Statistical Estimation and\n    Testing via the Ordered $l_1$ Norm.\n\n    Stephen Wright, Robert Nowak, and Mario Figueiredo. Sparse Reconstruction\n    by Separable Approximation. IEEE Transactions on Signal Processing, 2009,\n    Vol. 52, No. 7, 2479-2493.\n\n    Marcos Raydan. The Barzilai and Borwein Gradient Method for the Large Scale\n    Unconstrained Minimization Problem. SIAM J. Optim., 1997, Vol. 7, No. 1,\n    26-33.\n\n\n\n",
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        "license": "BSD (3-clause)",
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        "name": "brainowl",
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        "summary": "Classifier tuned for neuroimaging based on SpaRSA solver",
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