Metadata-Version: 2.1
Name: cdbw
Version: 0.1
Summary: Compute the CDbw validity index
Home-page: https://github.com/alashkov83/CDbw
Author: Sergey Rubinsky, Alexander Lashkov, Polina Eistrikh-Heller
Author-email: alashkov83@gmail.com
Maintainer: Alexander Lashkov
License: MIT License
Description: # CDbw
        Compute the S_Dbw validity index  
        S_Dbw validity index is defined by equation:
        ##### CDbw = compactness\*cohesion*separation
        **Highest value -> better clustering.**
        ______________________________________________
        
        #### Installation:
        
        ```shell
        pip install --upgrade cdbw
        ```
        
        ### Usage:
        
        ```python
        from cdbw import CDbw
        score = CDbw(X, labels, metric="euclidean", alg_noise='comb', 
             intra_dens_inf=False, s=3, multipliers=False)
        
        ```
        
        ### Parameters:
            X : array-like, shape (n_samples, n_features)
                List of n_features-dimensional data points. Each row corresponds
                to a single data point.
            labels : array-like, shape (n_samples,)
                Predicted labels for each sample.  (-1 - for noise)
            metric : str,
                The distance metric, can be ‘braycurtis’, ‘canberra’, ‘chebyshev’, ‘cityblock’, ‘correlation’,
                ‘cosine’, ‘dice’, ‘euclidean’, ‘hamming’, ‘jaccard’, ‘kulsinski’, ‘mahalanobis’, ‘matching’, ‘minkowski’,
                ‘rogerstanimoto’, ‘russellrao’, ‘seuclidean’, ‘sokalmichener’, ‘sokalsneath’, ‘sqeuclidean’, ‘wminkowski’,
                ‘yule’.
            alg_noise : str,
                Algorithm for recording noise points.
                'comb' - combining all noise points into one cluster (default)
                'sep' - definition of each noise point as a separate cluster
                'bind' -  binding of each noise point to the cluster nearest from it
                'filter' - filtering noise points
            intra_dens_inf : bool,
                If False (default) CDbw index = 0 for cohesion or compactness - inf or nan.
            s : int,
                Number of art representative points. (>2)
            multipliers : bool,
                Format of output. False (default) - only CDbw index, True - tuple (compactness, cohesion, separation, CDbw)
        
        ### Returns:
            cdbw : float,
                The resulting CDbw validity index.
        
        References:
        -----------
        1. M. Halkidi and M. Vazirgiannis, “A density-based cluster validity approach using multi-representatives”
                Pattern Recognition Letters 29 (2008) 773–786.
        
Keywords: clustering,cluster analysis,cluster validation
Platform: any
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved
Classifier: Programming Language :: Python
Classifier: Topic :: Software Development
Classifier: Topic :: Scientific/Engineering
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.4
Requires-Python: >=2.7
Description-Content-Type: text/markdown
