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()
