def __update_clusters(self):
        """!
        @brief Calculate distance (in line with specified metric) to each point from the each cluster. Nearest points
                are captured by according clusters and as a result clusters are updated.
        
        @return (list) Updated clusters as list of clusters. Each cluster contains indexes of objects from data.
        
        """
        
        clusters = [[] for _ in range(len(self.__centers))]
        
        dataset_differences = self.__calculate_dataset_difference(len(clusters))
        
        optimum_indexes = numpy.argmin(dataset_differences, axis=0)
        for index_point in range(len(optimum_indexes)):
            index_cluster = optimum_indexes[index_point]
            clusters[index_cluster].append(index_point)
        
        clusters = [cluster for cluster in clusters if len(cluster) > 0]

        return clusters
def __update_clusters(self):
        """!
        @brief Calculate distance (in line with specified metric) to each point from the each cluster. Nearest points
                are captured by according clusters and as a result clusters are updated.
        
        @return (list) Updated clusters as list of clusters. Each cluster contains indexes of objects from data.
        
        """
        
        clusters = [[] for _ in range(len(self.__centers))]
        
        dataset_differences = self.__calculate_dataset_difference(len(clusters))
        
        optimum_indexes = numpy.argmin(dataset_differences, axis=0)
        for index_point in range(len(optimum_indexes)):
            index_cluster = optimum_indexes[index_point]
            clusters[index_cluster].append(index_point)
        
        clusters = [cluster for cluster in clusters if len(cluster) > 0]

        return clusters
def __update_clusters ( self ) : clusters = [ [ ] for _ in range ( len ( self . __centers ) ) ] dataset_differences = self . __calculate_dataset_difference ( len ( clusters ) ) optimum_indexes = numpy . argmin ( dataset_differences , axis = 0 ) for index_point in range ( len ( optimum_indexes ) ) : index_cluster = optimum_indexes [ index_point ] clusters [ index_cluster ] . append ( index_point ) clusters = [ cluster for cluster in clusters if len ( cluster ) > 0 ] return clusters
!
        @brief Calculate distance (in line with specified metric) to each point from the each cluster. Nearest points
                are captured by according clusters and as a result clusters are updated.
        
        @return (list) Updated clusters as list of clusters. Each cluster contains indexes of objects from data.
!