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        "author": "Derrick Chambers",
        "author_email": "djachambeador@gmail.com",
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            "Development Status :: 4 - Beta",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: GNU Lesser General Public License v3 or later (LGPLv3+)",
            "Programming Language :: Python :: 3.7",
            "Programming Language :: Python :: 3.8",
            "Programming Language :: Python :: 3.9",
            "Topic :: Scientific/Engineering"
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        "description": "# DBSCAN1D\n\n[![Coverage](https://codecov.io/gh/d-chambers/dbscan1d/branch/master/graph/badge.svg)](https://codecov.io/gh/d-chambers/dbscan1d)\n[![Supported Versions](https://img.shields.io/pypi/pyversions/dbscan1d.svg)](https://pypi.python.org/pypi/dbscan1d)\n[![PyPI](https://pepy.tech/badge/dbscan1d)](https://pepy.tech/project/dbscan1d)\n[![Licence](https://www.gnu.org/graphics/lgplv3-88x31.png)](https://www.gnu.org/licenses/lgpl.html)\n\ndbscan1d is a 1D implementation of the [DBSCAN algorithm](https://en.wikipedia.org/wiki/DBSCAN). It was created to efficiently\npreform clustering on large 1D arrays.\n\n[Sci-kit Learn's DBSCAN implementation](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.DBSCAN.html) does\nnot have a special case for 1D, where calculating the full distance matrix is wasteful. It is much better to simply sort\nthe input array and performing efficient bisects for finding closest points. Here are the results of running the simple\nprofile script included with the package. In every case DBSCAN1D is much faster than scikit learn's implementation.\n\n![image](https://github.com/d-chambers/dbscan1d/raw/master/profile_results.png)\n\n## Installation\nSimply use pip to install dbscan1d:\n```bash\npip install dbscan1d\n```\nIt only requires numpy.\n\n## Quickstart\ndbscan1d is designed to be interchangable with sklearn's implementation in almost\nall cases. The exception is that the `weights` parameter is not yet supported.\n\n```python\nfrom sklearn.datasets import make_blobs\n\nfrom dbscan1d.core import DBSCAN1D\n\n# make blobs to test clustering\nX = make_blobs(1_000_000, centers=2, n_features=1)[0]\n\n# init dbscan object\ndbs = DBSCAN1D(eps=.5, min_samples=4)\n\n# get labels for each point\nlabels = dbs.fit_predict(X)\n\n# show core point indices\ndbs.core_sample_indices_\n\n# get values of core points\ndbs.components_\n```\n\n\n",
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