annoviko / pyclustering

                # dist = euclidean_distance(data[index_point], centers[index]);         # Slow solution

                dist = euclidean_distance_sqrt(self.__pointer_data[index_point], centers[index]);      # Fast solution

                if ( (dist < dist_optim) or (index is 0)):

                    index_optim = index;

                    dist_optim = dist;

            clusters[index_optim].append(index_point);

        return clusters;

    def __update_centers(self, clusters):

        """!

        @brief Updates centers of clusters in line with contained objects.

        @param[in] clusters (list): Clusters that contain indexes of objects from data.

        @return (list) Updated centers.

        """

        centers = [[] for i in range(len(clusters))];

        dimension = len(self.__pointer_data[0])

        for index in range(len(clusters)):

            point_sum = [0.0] * dimension;

        return clusters;

    def __update_centers(self):

        """!

        @brief Calculate centers of clusters in line with contained objects.

        @return (list) Updated centers as list of centers.

        """

        centers = [[] for i in range(len(self.__clusters))];

        for index in range(len(self.__clusters)):

            point_sum = [0] * len(self.__pointer_data[0]);

            for index_point in self.__clusters[index]:

                point_sum = list_math_addition(point_sum, self.__pointer_data[index_point]);

            centers[index] = list_math_division_number(point_sum, len(self.__clusters[index]));

        return centers;