def cluster(self,K): A = self.adjacency_matrix() c = cluster.kcluster(A,K) centers,self.Group_ids = c.cluster() max_cluster_size = c.maxsize self.distance_bandwidth = len(centers)+max_cluster_size-1 return centers
import sys import database import cluster import util import numpy as np import math ##################################################################### ## MAIN ##################################################################### if __name__ == "__main__": # parse if (len(sys.argv) < 2): print "usage: db_cluster.py database" sys.exit() dfile = sys.argv[1] K = int(sys.argv[2]) if not os.path.isfile(dfile): print dfile, "does not exist" sys.exit() stem = (dfile.split("."))[0] db = database.database() db.init(stem, "RMSD") A = db.adjacency_matrix() c = cluster.kcluster(A, K) c.cluster() c.report()
domainsFemale = roommate.arcConsistentDomains(femaleStudentsFeatures) #RUN BACKTRACKING_PAIRS ON PEOPLE -> ROOMMATES #get the set of roommate pairs malePairs= roommate.backtrackingPairs({}, domainsMale) #this is a dictionary of the roommate pairs! femalePairs = roommate.backtrackingPairs({}, domainsFemale) if(malePairs and femalePairs) : malePairs.update(femalePairs) pairs = malePairs #print"Roommate pairs:" #print pairs # print len(pairs) uniquePairs = roommate.uniquifyPairs(pairs) #get the clustering of the students clustering = cluster.kcluster(uniquePairs,studentDict,helper.NUM_SPOGROS) allStudentsClustering = deepcopy(clustering) for cluster1 in allStudentsClustering: for student in uniquePairs.keys(): if student in cluster1: cluster1.append(uniquePairs[student]) #print "Clustering for run"+str(runNum) #print allStudentsClustering #print uniquePairs # #use for testing (by just replacing uniquePairs with uniquePairsTEST below) # uniquePairsTEST = {} # for i in range(3) : # stud = uniquePairs.keys()[i] # uniquePairsTEST[stud] = uniquePairs[stud]
import database import cluster import util import numpy as np import math ##################################################################### ## MAIN ##################################################################### if __name__ == "__main__": # parse if (len(sys.argv) < 2): print "usage: db_cluster.py database" sys.exit() dfile = sys.argv[1] K = int(sys.argv[2]) if not os.path.isfile(dfile): print dfile,"does not exist" sys.exit() stem = (dfile.split("."))[0] db = database.database() db.init(stem,"RMSD") A = db.adjacency_matrix() c = cluster.kcluster(A,K) c.cluster() c.report()