Metadata-Version: 2.1
Name: fuzzy-c-means
Version: 0.0.6
Summary: A simple implementation of Fuzzy C-means algorithm.
Home-page: https://github.com/omadson/fuzzy-c-means
Author: Madson Dias
Author-email: madsonddias@gmail.com
License: MIT
Description: # fuzzy-c-means
        
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        `fuzzy-c-means` is a Python module implementing the [Fuzzy C-means][1] clustering algorithm.
        
        ## instalation
        the `fuzzy-c-means` package is available in [PyPI](https://pypi.org/project/fuzzy-c-means/). to install, simply type the following command:
        ```
        pip install fuzzy-c-means
        ```
        
        ## basic usage
        simple example of use the `fuzzy-c-means` to cluster a dataset in tree groups:
        ```Python
        from fcmeans import FCM
        from sklearn.datasets import make_blobs
        from matplotlib import pyplot as plt
        from seaborn import scatterplot as scatter
        
        
        # create artifitial dataset
        n_samples = 50000
        n_bins = 3  # use 3 bins for calibration_curve as we have 3 clusters here
        centers = [(-5, -5), (0, 0), (5, 5)]
        
        X,_ = make_blobs(n_samples=n_samples, n_features=2, cluster_std=1.0,
                          centers=centers, shuffle=False, random_state=42)
        
        # fit the fuzzy-c-means
        fcm = FCM(n_clusters=3)
        fcm.fit(X)
        
        # outputs
        fcm_centers = fcm.centers
        fcm_labels  = fcm.u.argmax(axis=1)
        
        
        # plot result
        %matplotlib inline
        f, axes = plt.subplots(1, 2, figsize=(11,5))
        scatter(X[:,0], X[:,1], ax=axes[0])
        scatter(X[:,0], X[:,1], ax=axes[1], hue=fcm_labels)
        scatter(fcm_centers[:,0], fcm_centers[:,1], ax=axes[1],marker="s",s=200)
        plt.show()
        ```
        
        ## how to cite fuzzy-c-means package
        if you use `fuzzy-c-means` package in your paper, please cite it in your publication.
        ```
        @misc{fuzzy-c-means,
            author       = "Madson Luiz Dantas Dias",
            year         = "2019",
            title        = "fuzzy-c-means: An implementation of Fuzzy $C$-means clustering algorithm.",
            url          = "https://github.com/omadson/fuzzy-c-means",
            institution  = "Federal University of Cear\'{a}, Department of Computer Science" 
        }
        ```
        
        ## contributing
        
        this project is open for contributions. here are some of the ways for you to contribute:
         - bug reports/fix
         - features requests
         - use-case demonstrations
        
        to make a contribution, just fork this repository, push the changes in your fork, open up an issue, and make a pull request!
        
        ## contributors
         - [Madson Dias](https://github.com/omadson)
        
        [1]: https://doi.org/10.1016/0098-3004(84)90020-7
        [2]: http://scikit-learn.org/
        
        
        
        
        
        
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Description-Content-Type: text/markdown
