annoviko / pyclustering

        @see get_clusters()

        """

        return self.__medians;

    def get_cluster_encoding(self):

        """!

        @brief Returns clustering result representation type that indicate how clusters are encoded.

        @return (type_encoding) Clustering result representation.

        @see get_clusters()

        """

        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;

        scores = [0.0] * len(clusters)     # splitting criterion

        dimension = len(self.__pointer_data[0]);

        # estimation of the noise variance in the data set

        sigma = 0.0;

        K = len(clusters);

        N = 0.0;

        for index_cluster in range(0, len(clusters), 1):

            for index_object in clusters[index_cluster]:

                sigma += (euclidean_distance(self.__pointer_data[index_object], centers[index_cluster]));  # It works

            N += len(clusters[index_cluster]);

        if (N - K != 0):

            sigma /= (N - K);

            # splitting criterion    

            for index_cluster in range(0, len(clusters), 1):

                n = len(clusters[index_cluster]);

                if (sigma > 0.0):

                    scores[index_cluster] = n * math.log(n) - n * math.log(N) - n * math.log(2.0 * numpy.pi) / 2.0 - n * dimension * math.log(sigma) / 2.0 - (n - K) / 2.0;

        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.

        """

        @see get_clusters()

        """

        return self.__centers;

    def get_cluster_encoding(self):

        """!

        @brief Returns clustering result representation type that indicate how clusters are encoded.

        @return (type_encoding) Clustering result representation.

        @see get_clusters()

        """

        return type_encoding.CLUSTER_INDEX_LIST_SEPARATION;

    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;

    def get_medoids(self):

        """!

        @brief Returns list of medoids of allocated clusters.

        @see process()

        @see get_clusters()

        """

        return self.__medoids;

    def get_cluster_encoding(self):

        """!

        @brief Returns clustering result representation type that indicate how clusters are encoded.

        @return (type_encoding) Clustering result representation.

        @see get_clusters()

        """

        return type_encoding.CLUSTER_INDEX_LIST_SEPARATION;

    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.