{
    "info": {
        "author": "",
        "author_email": "",
        "bugtrack_url": null,
        "classifiers": [
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3"
        ],
        "description": "Hyperdimensionality computing machine learning library.\n\nForked from https://gitlab.com/alehd/hd-lib\n\n# Installation and use\n\nFork this repository and put it in you projects folder or use pip or pipenv:\n```\npip install --user hyperdim\n```\n\nIf you are using keras or sklearn it will be easy to use hyperdim.\nTo use a model of dimensionality, say 10000:\n\n```\nfrom hyperdim.hdmodel import HDModel\nfrom hyperdim.utils import to_categorical\n\nimport numpy as np\n\n# Dummy datasets\nsamples = 100\nfeatures = 15\nclasses = 5\n\nx = np.random.random(size = (samples, features))\ny = [ int(np.random.random()*classes) for _ in x ]\ny = to_categorical(y)\n\nprint(x.shape) # (samples, features)\nprint(y.shape) # (samples, classes)\n\n# Build model\ndimensions = 10000\nmodel = HDModel(features, classes, d = dimensions)\n\n# Fit using 30% of the data for validation, using one_shot_fit only\nhistory = model.fit(x, y, validation_split = 0.3, epochs = 1)\n\nprint(history.history[\"acc\"])\nprint(history.history[\"val_acc\"])\n```\n\n\n",
        "description_content_type": "text/markdown",
        "docs_url": null,
        "download_url": "",
        "downloads": {
            "last_day": -1,
            "last_month": -1,
            "last_week": -1
        },
        "home_page": "https://gitlab.com/da_doomer/hyperdim",
        "keywords": "",
        "license": "",
        "maintainer": "",
        "maintainer_email": "",
        "name": "hyperdim",
        "package_url": "https://pypi.org/project/hyperdim/",
        "platform": "",
        "project_url": "https://pypi.org/project/hyperdim/",
        "project_urls": {
            "Homepage": "https://gitlab.com/da_doomer/hyperdim"
        },
        "release_url": "https://pypi.org/project/hyperdim/0.0.6/",
        "requires_dist": [
            "tqdm",
            "numpy",
            "sklearn"
        ],
        "requires_python": ">=3.6",
        "summary": "Hyperdimensionality computing machine learning library",
        "version": "0.0.6"
    },
    "last_serial": 5981129,
    "releases": {
        "0.0.6": [
            {
                "comment_text": "",
                "digests": {
                    "md5": "dfed7537d055cd51ff86b101ff4aa666",
                    "sha256": "ec6de4a80d462be977a1eb6a1687b9661cc0e9fc277f46bfb6201fdfe70a7148"
                },
                "downloads": -1,
                "filename": "hyperdim-0.0.6-py3-none-any.whl",
                "has_sig": false,
                "md5_digest": "dfed7537d055cd51ff86b101ff4aa666",
                "packagetype": "bdist_wheel",
                "python_version": "py3",
                "requires_python": ">=3.6",
                "size": 7707,
                "upload_time": "2019-09-28T22:01:46",
                "url": "https://files.pythonhosted.org/packages/dd/b5/f060fe1221db1370ac284a43d2e26d7a5371d0699c6ace91cc68f5a4c0e8/hyperdim-0.0.6-py3-none-any.whl"
            },
            {
                "comment_text": "",
                "digests": {
                    "md5": "d9fbb0d4f566e1e2f6b3f54ece0cca16",
                    "sha256": "f6264df6cc551784128a4a8744a72e302af92ee44456e4e59650ca5a86f14c82"
                },
                "downloads": -1,
                "filename": "hyperdim-0.0.6.tar.gz",
                "has_sig": false,
                "md5_digest": "d9fbb0d4f566e1e2f6b3f54ece0cca16",
                "packagetype": "sdist",
                "python_version": "source",
                "requires_python": ">=3.6",
                "size": 6183,
                "upload_time": "2019-09-28T22:01:48",
                "url": "https://files.pythonhosted.org/packages/2a/f2/55f1d4257bc83d7c02b73fa139b4e3474420438f7b1ef7f2f6f7f0b90c08/hyperdim-0.0.6.tar.gz"
            }
        ]
    },
    "urls": [
        {
            "comment_text": "",
            "digests": {
                "md5": "dfed7537d055cd51ff86b101ff4aa666",
                "sha256": "ec6de4a80d462be977a1eb6a1687b9661cc0e9fc277f46bfb6201fdfe70a7148"
            },
            "downloads": -1,
            "filename": "hyperdim-0.0.6-py3-none-any.whl",
            "has_sig": false,
            "md5_digest": "dfed7537d055cd51ff86b101ff4aa666",
            "packagetype": "bdist_wheel",
            "python_version": "py3",
            "requires_python": ">=3.6",
            "size": 7707,
            "upload_time": "2019-09-28T22:01:46",
            "url": "https://files.pythonhosted.org/packages/dd/b5/f060fe1221db1370ac284a43d2e26d7a5371d0699c6ace91cc68f5a4c0e8/hyperdim-0.0.6-py3-none-any.whl"
        },
        {
            "comment_text": "",
            "digests": {
                "md5": "d9fbb0d4f566e1e2f6b3f54ece0cca16",
                "sha256": "f6264df6cc551784128a4a8744a72e302af92ee44456e4e59650ca5a86f14c82"
            },
            "downloads": -1,
            "filename": "hyperdim-0.0.6.tar.gz",
            "has_sig": false,
            "md5_digest": "d9fbb0d4f566e1e2f6b3f54ece0cca16",
            "packagetype": "sdist",
            "python_version": "source",
            "requires_python": ">=3.6",
            "size": 6183,
            "upload_time": "2019-09-28T22:01:48",
            "url": "https://files.pythonhosted.org/packages/2a/f2/55f1d4257bc83d7c02b73fa139b4e3474420438f7b1ef7f2f6f7f0b90c08/hyperdim-0.0.6.tar.gz"
        }
    ]
}