def __calculate_total_wce(self):
        """!
        @brief Calculate total within cluster errors that is depend on metric that was chosen for K-Means algorithm.

        """

        dataset_differences = self.__calculate_dataset_difference(len(self.__clusters))

        self.__total_wce = 0
        for index_cluster in range(len(self.__clusters)):
            for index_point in self.__clusters[index_cluster]:
                self.__total_wce += dataset_differences[index_cluster][index_point]
def __calculate_total_wce(self):
        """!
        @brief Calculate total within cluster errors that is depend on metric that was chosen for K-Means algorithm.

        """

        dataset_differences = self.__calculate_dataset_difference(len(self.__clusters))

        self.__total_wce = 0
        for index_cluster in range(len(self.__clusters)):
            for index_point in self.__clusters[index_cluster]:
                self.__total_wce += dataset_differences[index_cluster][index_point]
def __calculate_total_wce ( self ) : dataset_differences = self . __calculate_dataset_difference ( len ( self . __clusters ) ) self . __total_wce = 0 for index_cluster in range ( len ( self . __clusters ) ) : for index_point in self . __clusters [ index_cluster ] : self . __total_wce += dataset_differences [ index_cluster ] [ index_point ]
!
        @brief Calculate total within cluster errors that is depend on metric that was chosen for K-Means algorithm.
!