{
    "info": {
        "author": "Tahmid Mehdi",
        "author_email": "tfmehdi@cs.toronto.edu",
        "bugtrack_url": null,
        "classifiers": [
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: GNU General Public License v3 (GPLv3)",
            "Natural Language :: English",
            "Programming Language :: Python :: 3.6",
            "Topic :: Scientific/Engineering :: Artificial Intelligence"
        ],
        "description": "Implements Dirichlet Process Heterogeneous Mixtures of exponential family distributions for clustering heterogeneous data without choosing the number of clusters. Inference can be performed with Gibbs sampling or coordinate ascent mean-field variational inference. For semi-supervised learning, Gibbs sampling supports must-link and cannot-link constraints. A novel variational inference algorithm was derived to handle must-link constraints.",
        "description_content_type": "",
        "docs_url": null,
        "download_url": "https://github.com/tahmidmehdi/dphmix/archive/v0.1.tar.gz",
        "downloads": {
            "last_day": -1,
            "last_month": -1,
            "last_week": -1
        },
        "home_page": "https://github.com/tahmidmehdi/dphmix",
        "keywords": "",
        "license": "GNU GPL v3.0",
        "maintainer": "",
        "maintainer_email": "",
        "name": "dphmix",
        "package_url": "https://pypi.org/project/dphmix/",
        "platform": "",
        "project_url": "https://pypi.org/project/dphmix/",
        "project_urls": {
            "Download": "https://github.com/tahmidmehdi/dphmix/archive/v0.1.tar.gz",
            "Homepage": "https://github.com/tahmidmehdi/dphmix"
        },
        "release_url": "https://pypi.org/project/dphmix/0.1.0/",
        "requires_dist": null,
        "requires_python": "",
        "summary": "Unsupervised and Semi-supervised Dirichlet Process Heterogeneous Mixtures",
        "version": "0.1.0"
    },
    "last_serial": 4181835,
    "releases": {
        "0.1.0": [
            {
                "comment_text": "",
                "digests": {
                    "md5": "478a5e2f5290e22d27c67897ccb9de21",
                    "sha256": "9020e616adeb1bba7d2b0dc3eb0b70eb3cfd606a9c2a0b1df08a425c5a11178e"
                },
                "downloads": -1,
                "filename": "dphmix-0.1.0.tar.gz",
                "has_sig": false,
                "md5_digest": "478a5e2f5290e22d27c67897ccb9de21",
                "packagetype": "sdist",
                "python_version": "source",
                "requires_python": null,
                "size": 15010,
                "upload_time": "2018-08-17T21:24:00",
                "url": "https://files.pythonhosted.org/packages/3d/03/ce20e30044c147232c8db68d138d84b24f7dd8a01c43775e35994d2ddec6/dphmix-0.1.0.tar.gz"
            }
        ]
    },
    "urls": [
        {
            "comment_text": "",
            "digests": {
                "md5": "478a5e2f5290e22d27c67897ccb9de21",
                "sha256": "9020e616adeb1bba7d2b0dc3eb0b70eb3cfd606a9c2a0b1df08a425c5a11178e"
            },
            "downloads": -1,
            "filename": "dphmix-0.1.0.tar.gz",
            "has_sig": false,
            "md5_digest": "478a5e2f5290e22d27c67897ccb9de21",
            "packagetype": "sdist",
            "python_version": "source",
            "requires_python": null,
            "size": 15010,
            "upload_time": "2018-08-17T21:24:00",
            "url": "https://files.pythonhosted.org/packages/3d/03/ce20e30044c147232c8db68d138d84b24f7dd8a01c43775e35994d2ddec6/dphmix-0.1.0.tar.gz"
        }
    ]
}