pyclustering/cluster/silhouette.py
def __process_by_python(self):
        """!
        @brief Performs processing using python code.

        """
        self.__scores = {}

        for k in range(self.__kmin, self.__kmax):
            clusters = self.__calculate_clusters(k)
            if len(clusters) != k:
                self.__scores[k] = float('nan')
                continue

            score = silhouette(self.__data, clusters).process().get_score()

            self.__scores[k] = sum(score) / len(score)

            if self.__scores[k] > self.__score:
                self.__score = self.__scores[k]
                self.__amount = k
def __process_by_python(self):
        """!
        @brief Performs processing using python code.

        """
        self.__scores = {}

        for k in range(self.__kmin, self.__kmax):
            clusters = self.__calculate_clusters(k)
            if len(clusters) != k:
                self.__scores[k] = float('nan')
                continue

            score = silhouette(self.__data, clusters).process().get_score()

            self.__scores[k] = sum(score) / len(score)

            if self.__scores[k] > self.__score:
                self.__score = self.__scores[k]
                self.__amount = k
def __process_by_python ( self ) : self . __scores = { } for k in range ( self . __kmin , self . __kmax ) : clusters = self . __calculate_clusters ( k ) if len ( clusters ) != k : self . __scores [ k ] = float ( 'nan' ) continue score = silhouette ( self . __data , clusters ) . process ( ) . get_score ( ) self . __scores [ k ] = sum ( score ) / len ( score ) if self . __scores [ k ] > self . __score : self . __score = self . __scores [ k ] self . __amount = k
!
        @brief Performs processing using python code.
!