{
    "info": {
        "author": "Paul Harrison",
        "author_email": "pfh at logarithmic dot net",
        "bugtrack_url": null,
        "classifiers": [],
        "description": "A re-implementation of the C \"Vanilla-Snob\" MML\n        mixture-modeller by Chris Wallace. Little Snob is a\n        library for unsupervised classification of a set of data\n        into classes. It can handle continuous or discrete data,\n        and is capable of determining the optimal number of classes\n        to use on its own.",
        "description_content_type": null,
        "docs_url": null,
        "download_url": null,
        "downloads": {
            "last_day": -1,
            "last_month": -1,
            "last_week": -1
        },
        "home_page": "http://www.logarithmic.net/pfh/Little_Snob",
        "keywords": null,
        "license": "GPL",
        "maintainer": null,
        "maintainer_email": null,
        "name": "Little-Snob",
        "package_url": "https://pypi.org/project/Little-Snob/",
        "platform": null,
        "project_url": "https://pypi.org/project/Little-Snob/",
        "project_urls": {
            "Homepage": "http://www.logarithmic.net/pfh/Little_Snob"
        },
        "release_url": "https://pypi.org/project/Little-Snob/0.2/",
        "requires_dist": null,
        "requires_python": null,
        "summary": "A simple library for unsupervised classification of data.",
        "version": "0.2"
    },
    "last_serial": 1217,
    "releases": {
        "0.2": []
    },
    "urls": []
}