{
    "info": {
        "author": "Adrian Goedeckemeyer",
        "author_email": "adrian+pypi@minerva.kgi.edu",
        "bugtrack_url": null,
        "classifiers": [],
        "description": ".. image:: https://readthedocs.org/projects/afkmc2/badge/?version=latest\r\n    :target: http://afkmc2.readthedocs.io/en/latest/?badge=latest\r\n    :alt: Documentation Status\r\n\r\nAssumption Free KMeans Monte Carlo\r\n==================================\r\n\r\nThis package contains sklearn compatible python implementations of various K-Means seeding algorithms.\r\n\r\nThe package was inspired by the AFKMC^2 algorithm detailed in\r\n\r\n    |   **Fast and Provably Good Seedings for k-Means**\r\n    |   Olivier Bachem, Mario Lucic, S. Hamed Hassani and Andreas Krause\r\n    |   In *Neural Information Processing Systems* (NIPS), 2016.\r\n    |   https://las.inf.ethz.ch/files/bachem16fast.pdf\r\n\r\nThe algorithm uses Monte Carlo Markov Chain to quickly find good seedings for KMeans and offers a runtime improvement over the common K-Means++ algorithm.\r\n\r\nUsage\r\n-----\r\n\r\nUsing this package to get seedings for KMeans in sklearn is as simple as::\r\n\r\n    import afkmc2\r\n    X = np.array([[1, 2], [1, 4], [1, 0],\r\n                 [4, 2], [4, 4], [4, 0]])\r\n    seeds = afkmc2.afkmc2(X, 2)\r\n\r\n    from sklearn.custer import KMeans\r\n    model = KMeans(n_clusters=2, init=seeds).fit(X)\r\n    print model.cluster_centers_\r\n\r\nInstallation\r\n------------\r\n\r\nQuickly install afkmc2 by running (coming soon)::\r\n\r\n    pip install afkmc2\r\n\r\nContribute\r\n----------\r\n\r\n* Issue Tracker: https://github.com/adriangoe/afkmc2/issues\r\n\r\nSupport\r\n-------\r\n\r\nYou can reach out to me through https://adriangoe.me/.\r\n\r\n\r\nLicense\r\n-------\r\n\r\nThe project is licensed under the MIT License.",
        "description_content_type": null,
        "docs_url": null,
        "download_url": "https://github.com/adriangoe/afkmc2/archive/0.1.tar.gz",
        "downloads": {
            "last_day": -1,
            "last_month": -1,
            "last_week": -1
        },
        "home_page": "http://afkmc2.readthedocs.io/en/latest/index.html",
        "keywords": "kmeans,seeding,sklearn,numpy",
        "license": "UNKNOWN",
        "maintainer": "",
        "maintainer_email": "",
        "name": "afkmc2",
        "package_url": "https://pypi.org/project/afkmc2/",
        "platform": "UNKNOWN",
        "project_url": "https://pypi.org/project/afkmc2/",
        "project_urls": {
            "Download": "https://github.com/adriangoe/afkmc2/archive/0.1.tar.gz",
            "Homepage": "http://afkmc2.readthedocs.io/en/latest/index.html"
        },
        "release_url": "https://pypi.org/project/afkmc2/0.1/",
        "requires_dist": null,
        "requires_python": null,
        "summary": "Assumption Free and Efficient K-Means Seeding",
        "version": "0.1"
    },
    "last_serial": 2817227,
    "releases": {
        "0.1": [
            {
                "comment_text": "",
                "digests": {
                    "md5": "1dd3439a768ecb54923d7a623bf8bb5d",
                    "sha256": "4a6a48948c64f2bc580cd780087162ff3ae675ae3f3beb98b001b94628ff9b8a"
                },
                "downloads": -1,
                "filename": "afkmc2-0.1.tar.gz",
                "has_sig": false,
                "md5_digest": "1dd3439a768ecb54923d7a623bf8bb5d",
                "packagetype": "sdist",
                "python_version": "source",
                "requires_python": null,
                "size": 4083,
                "upload_time": "2017-04-20T15:05:59",
                "url": "https://files.pythonhosted.org/packages/4f/9f/4abca0daf62c08bac4cd751d6bf4dd6e1ee8f2962df9f7f89b0cfb21d86d/afkmc2-0.1.tar.gz"
            }
        ]
    },
    "urls": [
        {
            "comment_text": "",
            "digests": {
                "md5": "1dd3439a768ecb54923d7a623bf8bb5d",
                "sha256": "4a6a48948c64f2bc580cd780087162ff3ae675ae3f3beb98b001b94628ff9b8a"
            },
            "downloads": -1,
            "filename": "afkmc2-0.1.tar.gz",
            "has_sig": false,
            "md5_digest": "1dd3439a768ecb54923d7a623bf8bb5d",
            "packagetype": "sdist",
            "python_version": "source",
            "requires_python": null,
            "size": 4083,
            "upload_time": "2017-04-20T15:05:59",
            "url": "https://files.pythonhosted.org/packages/4f/9f/4abca0daf62c08bac4cd751d6bf4dd6e1ee8f2962df9f7f89b0cfb21d86d/afkmc2-0.1.tar.gz"
        }
    ]
}