{
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        "author": "Madson Dias",
        "author_email": "madsonddias@gmail.com",
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            "Development Status :: 2 - Pre-Alpha",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7"
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        "description": "# fuzzy-c-means\n\n![GitHub](https://img.shields.io/github/license/omadson/fuzzy-c-means.svg)\n[![PyPI](https://img.shields.io/pypi/v/fuzzy-c-means.svg)](http://pypi.org/project/fuzzy-c-means/)\n[![GitHub commit activity](https://img.shields.io/github/commit-activity/w/omadson/fuzzy-c-means.svg)](https://github.com/omadson/fuzzy-c-means/pulse)\n[![GitHub last commit](https://img.shields.io/github/last-commit/omadson/fuzzy-c-means.svg)](https://github.com/omadson/fuzzy-c-means/commit/master)\n\n\n`fuzzy-c-means` is a Python module implementing the [Fuzzy C-means][1] clustering algorithm.\n\n## instalation\nthe `fuzzy-c-means` package is available in [PyPI](https://pypi.org/project/fuzzy-c-means/). to install, simply type the following command:\n```\npip install fuzzy-c-means\n```\n\n## basic usage\nsimple example of use the `fuzzy-c-means` to cluster a dataset in tree groups:\n```Python\nfrom fcmeans import FCM\nfrom sklearn.datasets import make_blobs\nfrom matplotlib import pyplot as plt\nfrom seaborn import scatterplot as scatter\n\n\n# create artifitial dataset\nn_samples = 50000\nn_bins = 3  # use 3 bins for calibration_curve as we have 3 clusters here\ncenters = [(-5, -5), (0, 0), (5, 5)]\n\nX,_ = make_blobs(n_samples=n_samples, n_features=2, cluster_std=1.0,\n                  centers=centers, shuffle=False, random_state=42)\n\n# fit the fuzzy-c-means\nfcm = FCM(n_clusters=3)\nfcm.fit(X)\n\n# outputs\nfcm_centers = fcm.centers\nfcm_labels  = fcm.u.argmax(axis=1)\n\n\n# plot result\n%matplotlib inline\nf, axes = plt.subplots(1, 2, figsize=(11,5))\nscatter(X[:,0], X[:,1], ax=axes[0])\nscatter(X[:,0], X[:,1], ax=axes[1], hue=fcm_labels)\nscatter(fcm_centers[:,0], fcm_centers[:,1], ax=axes[1],marker=\"s\",s=200)\nplt.show()\n```\n\n## how to cite fuzzy-c-means package\nif you use `fuzzy-c-means` package in your paper, please cite it in your publication.\n```\n@misc{fuzzy-c-means,\n    author       = \"Madson Luiz Dantas Dias\",\n    year         = \"2019\",\n    title        = \"fuzzy-c-means: An implementation of Fuzzy $C$-means clustering algorithm.\",\n    url          = \"https://github.com/omadson/fuzzy-c-means\",\n    institution  = \"Federal University of Cear\\'{a}, Department of Computer Science\" \n}\n```\n\n## contributing\n\nthis project is open for contributions. here are some of the ways for you to contribute:\n - bug reports/fix\n - features requests\n - use-case demonstrations\n\nto make a contribution, just fork this repository, push the changes in your fork, open up an issue, and make a pull request!\n\n## contributors\n - [Madson Dias](https://github.com/omadson)\n\n[1]: https://doi.org/10.1016/0098-3004(84)90020-7\n[2]: http://scikit-learn.org/\n\n\n\n\n\n\n\n",
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