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        "author": "Justin Shenk",
        "author_email": "shenkjustin@gmail.com",
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        "classifiers": [
            "Intended Audience :: Developers",
            "Intended Audience :: Education",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.6",
            "Topic :: Scientific/Engineering :: Mathematics",
            "Topic :: Software Development :: Libraries",
            "Topic :: Software Development :: Libraries :: Python Modules"
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        "description": "# Closest Pairs :triangular_ruler:\n\nFind the closest pairs in an array.\n\n### Getting Started\n\n```bash\npip install closest_pairs\n```\n\nor install from source:\n```bash\ngit clone https://github.com/justinshenk/closest-pairs\ncd closest_pairs\npip install .\n```\n\n### How to use\n\n```python\nimport closest_pairs\n\n# X is an n x m numpy array\npairs, distances = closest_pairs.solve(X, n=1)\n\n```\n\nYou can specify how many pairs you want to identify with `n`.\n\n\n### Example\n```python\nimport closest_pairs\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# Create dataset\nX = np.random.random((100,2))\npairs, distance = closest_pairs.solve(X, n=1)\n\n# Plot points\nz, y = np.split(X, 2, axis=1)\nfig, ax = plt.subplots()\nax.scatter(z, y) \n\nfor i, txt in enumerate(X): \n    if i in pairs: \n        ax.annotate(i, (z[i], y[i]), color='red') \n    else: \n        ax.annotate(i, (z[i], y[i])) \n```\n\nCheck pairs:\n```ipython\nIn [10]: pairs                                                                                                                                \nOut[10]: \narray([[[ 7],\n        [16]],\n\n       [[96],\n        [50]]])\n\n```\n\nOutput:\n![example_plot](example_plot.png)\n\n\n### Caveats\n`closest_pairs` will reduce the dimensionality with PCA of your data to two-dimensions for faster processing.\n\nIt also removes the first point in a pair if `n`>1. In rare cases this leads to false negatives if the data is highly overlapping.\n\n\n### Credit and Explanation\n\nPython code modified from [Andriy Lazorenko](https://medium.com/@andriylazorenko/closest-pair-of-points-in-python-79e2409fc0b2), packaged and made useful for >2 features by Justin Shenk.\n\n\n",
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        "keywords": "mathematics geometry",
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