annoviko / pyclustering

            raise TypeError("Unknown type of data is specified '%s'" % self.__data_type)

    def __update_clusters(self):

        """!

        @brief Calculate distance to each point from the each cluster. 

        @details Nearest points are captured by according clusters and as a result clusters are updated.

        @return (list) updated clusters as list of clusters where each cluster contains indexes of objects from data.

        """

        clusters = [[self.__medoid_indexes[i]] for i in range(len(self.__medoid_indexes))]

        for index_point in range(len(self.__pointer_data)):

            if index_point in self.__medoid_indexes:

                continue

            index_optim = -1

            dist_optim = float('Inf')

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

                dist = self.__distance_calculator(index_point, self.__medoid_indexes[index])

                if dist < dist_optim:

                    index_optim = index

                    dist_optim = dist

            clusters[index_optim].append(index_point)

        return clusters

    def __update_medoids(self):

        return type_encoding.CLUSTER_INDEX_LIST_SEPARATION

    def __update_clusters(self):

        """!

        @brief Calculate Manhattan distance to each point from the each cluster. 

        @details Nearest points are captured by according clusters and as a result clusters are updated.

        @return (list) updated clusters as list of clusters where each cluster contains indexes of objects from data.

        """

        clusters = [[] for i in range(len(self.__medians))]

        for index_point in range(len(self.__pointer_data)):

            index_optim = -1

            dist_optim = 0.0

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

                dist = self.__metric(self.__pointer_data[index_point], self.__medians[index])

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

                    index_optim = index

                    dist_optim = dist

            clusters[index_optim].append(index_point)

        # If cluster is not able to capture object it should be removed

        clusters = [cluster for cluster in clusters if len(cluster) > 0]

        return clusters

    def __update_medians(self):