annoviko / pyclustering

        return self.__medians;

    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 = euclidean_distance_sqrt(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):

        return sum(scores);

    def __update_clusters(self, centers, available_indexes = None):

        """!

        @brief Calculates Euclidean distance to each point from the each cluster.

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

        @param[in] centers (list): Coordinates of centers of clusters that are represented by list: [center1, center2, ...].

        @param[in] available_indexes (list): Indexes that defines which points can be used from imput data, if None - then all points are used.

        @return (list) Updated clusters.

        """

        bypass = None;

        if (available_indexes is None):

            bypass = range(len(self.__pointer_data));

        else:

            bypass = available_indexes;

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

        for index_point in bypass:

            index_optim = -1;

            dist_optim = 0.0;

            for index in range(len(centers)):

                # 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):

        return self.__centers;

    def __update_clusters(self):

        """!

        @brief Calculate Euclidean distance 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 i in range(len(self.__centers))];

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

            index_optim = -1;

            dist_optim = 0.0;

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

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

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

                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_centers(self):

        return self.__medoids;

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

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

            index_optim = -1;

            dist_optim = 0.0;

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

                dist = euclidean_distance_sqrt(self.__pointer_data[index_point], self.__medoids[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_medoids(self):