S_Dbw | S_Dbw validity index

 by   alashkov83 Jupyter Notebook Version: 0.4.0 License: MIT

kandi X-RAY | S_Dbw Summary

kandi X-RAY | S_Dbw Summary

S_Dbw is a Jupyter Notebook library. S_Dbw has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can download it from GitHub.

S_Dbw validity index. Adapted for DBSCAN (and similar)
Support
    Quality
      Security
        License
          Reuse

            kandi-support Support

              S_Dbw has a low active ecosystem.
              It has 5 star(s) with 3 fork(s). There are no watchers for this library.
              OutlinedDot
              It had no major release in the last 12 months.
              There are 1 open issues and 4 have been closed. On average issues are closed in 8 days. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of S_Dbw is 0.4.0

            kandi-Quality Quality

              S_Dbw has no bugs reported.

            kandi-Security Security

              S_Dbw has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

            kandi-License License

              S_Dbw is licensed under the MIT License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              S_Dbw releases are available to install and integrate.
              Installation instructions are not available. Examples and code snippets are available.

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            S_Dbw Key Features

            No Key Features are available at this moment for S_Dbw.

            S_Dbw Examples and Code Snippets

            S_Dbw,Usage
            Jupyter Notebookdot img1Lines of Code : 7dot img1License : Permissive (MIT)
            copy iconCopy
            from s_dbw import S_Dbw
            score = S_Dbw(X, labels, centers_id=None, method='Tong', alg_noise='bind',
            centr='mean', nearest_centr=True, metric='euclidean')
            
            
            from s_dbw import SD
            score = SD(X, labels, k=1.0, centers_id=None,  alg_noise='bind',centr='mea  
            S_Dbw,Installation
            Jupyter Notebookdot img2Lines of Code : 1dot img2License : Permissive (MIT)
            copy iconCopy
            pip install --upgrade s-dbw
              

            Community Discussions

            No Community Discussions are available at this moment for S_Dbw.Refer to stack overflow page for discussions.

            Community Discussions, Code Snippets contain sources that include Stack Exchange Network

            Vulnerabilities

            No vulnerabilities reported

            Install S_Dbw

            You can download it from GitHub.

            Support

            For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .
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            CLONE
          • HTTPS

            https://github.com/alashkov83/S_Dbw.git

          • CLI

            gh repo clone alashkov83/S_Dbw

          • sshUrl

            git@github.com:alashkov83/S_Dbw.git

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