pyclustering/cluster/silhouette.py
def __calculate_dataset_difference(self, index_point):
        """!
        @brief Calculate distance from each object to specified object.

        @param[in] index_point (uint): Index point for which difference with other points is calculated.

        @return (list) Distance to each object from input data from the specified.

        """

        if self.__metric.get_type() != type_metric.USER_DEFINED:
            dataset_differences = self.__metric(self.__data, self.__data[index_point])
        else:
            dataset_differences = [self.__metric(point, self.__data[index_point]) for point in self.__data]

        return dataset_differences
def __calculate_dataset_difference(self, index_point):
        """!
        @brief Calculate distance from each object to specified object.

        @param[in] index_point (uint): Index point for which difference with other points is calculated.

        @return (list) Distance to each object from input data from the specified.

        """

        if self.__metric.get_type() != type_metric.USER_DEFINED:
            dataset_differences = self.__metric(self.__data, self.__data[index_point])
        else:
            dataset_differences = [self.__metric(point, self.__data[index_point]) for point in self.__data]

        return dataset_differences
def __calculate_dataset_difference ( self , index_point ) : if self . __metric . get_type ( ) != type_metric . USER_DEFINED : dataset_differences = self . __metric ( self . __data , self . __data [ index_point ] ) else : dataset_differences = [ self . __metric ( point , self . __data [ index_point ] ) for point in self . __data ] return dataset_differences
!
        @brief Calculate distance from each object to specified object.

        @param[in] index_point (uint): Index point for which difference with other points is calculated.

        @return (list) Distance to each object from input data from the specified.
!