pyclustering/cluster/agglomerative.py
def __merge_by_average_link(self):
        """!
        @brief Merges the most similar clusters in line with average link type.
        
        """
        
        minimum_average_distance = float('Inf');
        
        for index_cluster1 in range(0, len(self.__clusters)):
            for index_cluster2 in range(index_cluster1 + 1, len(self.__clusters)):
                
                # Find farthest objects
                candidate_average_distance = 0.0;
                for index_object1 in self.__clusters[index_cluster1]:
                    for index_object2 in self.__clusters[index_cluster2]:
                        candidate_average_distance += euclidean_distance_square(self.__pointer_data[index_object1], self.__pointer_data[index_object2]);
                
                candidate_average_distance /= (len(self.__clusters[index_cluster1]) + len(self.__clusters[index_cluster2]));
                
                if (candidate_average_distance < minimum_average_distance):
                    minimum_average_distance = candidate_average_distance;
                    indexes = [index_cluster1, index_cluster2];
        
        self.__clusters[indexes[0]] += self.__clusters[indexes[1]];  
        self.__clusters.pop(indexes[1]);
def __merge_by_average_link(self):
        """!
        @brief Merges the most similar clusters in line with average link type.
        
        """
        
        minimum_average_distance = float('Inf');
        
        for index_cluster1 in range(0, len(self.__clusters)):
            for index_cluster2 in range(index_cluster1 + 1, len(self.__clusters)):
                
                # Find farthest objects
                candidate_average_distance = 0.0;
                for index_object1 in self.__clusters[index_cluster1]:
                    for index_object2 in self.__clusters[index_cluster2]:
                        candidate_average_distance += euclidean_distance_square(self.__pointer_data[index_object1], self.__pointer_data[index_object2]);
                
                candidate_average_distance /= (len(self.__clusters[index_cluster1]) + len(self.__clusters[index_cluster2]));
                
                if (candidate_average_distance < minimum_average_distance):
                    minimum_average_distance = candidate_average_distance;
                    indexes = [index_cluster1, index_cluster2];
        
        self.__clusters[indexes[0]] += self.__clusters[indexes[1]];  
        self.__clusters.pop(indexes[1]);
def __merge_by_average_link ( self ) : minimum_average_distance = float ( 'Inf' ) for index_cluster1 in range ( 0 , len ( self . __clusters ) ) : for index_cluster2 in range ( index_cluster1 + 1 , len ( self . __clusters ) ) : # Find farthest objects candidate_average_distance = 0.0 for index_object1 in self . __clusters [ index_cluster1 ] : for index_object2 in self . __clusters [ index_cluster2 ] : candidate_average_distance += euclidean_distance_square ( self . __pointer_data [ index_object1 ] , self . __pointer_data [ index_object2 ] ) candidate_average_distance /= ( len ( self . __clusters [ index_cluster1 ] ) + len ( self . __clusters [ index_cluster2 ] ) ) if ( candidate_average_distance < minimum_average_distance ) : minimum_average_distance = candidate_average_distance indexes = [ index_cluster1 , index_cluster2 ] self . __clusters [ indexes [ 0 ] ] += self . __clusters [ indexes [ 1 ] ] self . __clusters . pop ( indexes [ 1 ] )
!
        @brief Merges the most similar clusters in line with average link type.
!