annoviko / pyclustering

        if (N - K > 0):

            sigma_sqrt /= (N - K);

            p = (K - 1) + dimension * K + 1;

            # splitting criterion    

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

                n = len(clusters[index_cluster]);

                L = n * log(n) - n * log(N) - n * 0.5 * log(2.0 * numpy.pi) - n * dimension * 0.5 * log(sigma_sqrt) - (n - K) * 0.5;

                # BIC calculation

                scores[index_cluster] = L - p * 0.5 * log(N);

        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.

        """

    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;

            clusters[index_optim].append(index_point);

        return clusters;

    def __update_medoids(self):

        """!

        @brief Find medoids of clusters in line with contained objects.

        @return (list) list of medoids for current number of clusters.

        """

        medoids = [[] for _ in range(len(self.__clusters))];

        medoid_indexes = [-1] * len(self.__clusters);

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

            medoid_index = median(self.__pointer_data, self.__clusters[index]);

            medoids[index] = self.__pointer_data[medoid_index];

            medoid_indexes[index] = medoid_index;

        return medoids, medoid_indexes;