def silhouette_score(self, data,  metric='euclidean', sample_size=None, random_state=None, **kwds):
        """
        Computes the mean Silhouette Coefficient of all samples (implicit evaluation)

        :param data: The data that the clusters are generated from
        :param metric: the pairwise distance metric
        :param sample_size: the size of the sample to use computing the Silhouette Coefficient
        :param random_state: If an integer is given then it fixes its seed otherwise random.
        :param kwds: any further parameters that are passed to the distance function
        :return: the mean Silhouette Coefficient of all samples
        """
        return silhouette_score(data, self.get_labels(self), metric, sample_size, random_state, **kwds)
def silhouette_score ( self , data , metric = 'euclidean' , sample_size = None , random_state = None , ** kwds ) : return silhouette_score ( data , self . get_labels ( self ) , metric , sample_size , random_state , ** kwds )
Computes the mean Silhouette Coefficient of all samples (implicit evaluation)

        :param data: The data that the clusters are generated from
        :param metric: the pairwise distance metric
        :param sample_size: the size of the sample to use computing the Silhouette Coefficient
        :param random_state: If an integer is given then it fixes its seed otherwise random.
        :param kwds: any further parameters that are passed to the distance function
        :return: the mean Silhouette Coefficient of all samples