# -*- coding: utf-8 -*- import sys import getopt import re import math import ir_tools as ir print ir.cosine(1,3)
# -*- coding: utf-8 -*- import sys import getopt import re import math import ir_tools as ir print ir.cosine(1, 3)
def main(argv=None):
if argv == None:
argv = sys.argv
K = int(argv[1])
#simple HAC
print "----- HAC Clustering -----"
#initialize
cluster = {}
C = [[0 for x in range(N)] for x in range(N)]
sys.stdout.write("initializing similarity matrix...[%.2f%%]" % (0 / 10.95))
sys.stdout.flush()
if os.path.exists("./sim_matrix.dat"):
f = open("sim_matrix.dat", "r")
lines = f.readlines()
for n in xrange(0, N):
if os.path.exists("./sim_matrix.dat"):
line = lines[n]
vals = line.replace("\n", "").split(" ")
i = 0
for val in vals:
C[n][i] = float(val)
i = i + 1
else:
for i in xrange(0, N):
if n > i:
C[n][i] = C[i][n]
elif n == i:
C[n][i] = 1.0
else:
C[n][i] = ir.cosine(n + 1, i + 1)
I.append(1)
cluster[n] = [n]
sys.stdout.write("\rinitializing similarity matrix... [%.2f%%]" %
((n + 1) / 10.95))
sys.stdout.flush()
sys.stdout.write("\n")
if os.path.exists("./sim_matrix.dat"):
f.close()
else:
f = open("sim_matrix.dat", "w")
for c in C:
f.write(" ".join(str(v) for v in c))
f.write("\n")
f.close()
merge_index = []
#print C
sys.stdout.write("\rclustering...[%.2f%%]" % (0))
sys.stdout.flush()
for k in xrange(0, N - K):
pair = argmax(C)
cluster[pair[0]].append(pair[1])
for j in xrange(0, N):
val = cluster_sim(cluster[j], cluster[pair[0]], C)
C[pair[0]][j] = val
C[j][pair[0]] = val
sys.stdout.write("\rclustering...[%.2f%%]" %
(float(k * N + j + 1) * 100 / (N * (N - K))))
sys.stdout.flush()
I[pair[1]] = 0
merge_index.append(pair[1])
sys.stdout.write("\n")
print "----- save in the file -----"
save = open(str(K) + ".txt", "w")
for index in cluster:
if index not in merge_index:
for docid in cluster[index]:
save.write(str(docid + 1))
save.write("\n")
save.write("\n")
save.close()
print "finish clustering ^^"
def main(argv=None):
if argv == None:
argv = sys.argv
K = int(argv[1])
#simple HAC
print "----- HAC Clustering -----"
#initialize
cluster = {}
C = [[0 for x in range(N)] for x in range(N)]
sys.stdout.write("initializing similarity matrix...[%.2f%%]" % (0/10.95))
sys.stdout.flush()
if os.path.exists("./sim_matrix.dat"):
f = open("sim_matrix.dat","r")
lines = f.readlines()
for n in xrange(0,N):
if os.path.exists("./sim_matrix.dat"):
line = lines[n]
vals = line.replace("\n","").split(" ")
i=0
for val in vals:
C[n][i] = float(val)
i=i+1
else:
for i in xrange(0,N):
if n > i:
C[n][i] = C[i][n]
elif n == i:
C[n][i] = 1.0
else:
C[n][i] = ir.cosine(n+1,i+1)
I.append(1)
cluster[n] = [n]
sys.stdout.write("\rinitializing similarity matrix... [%.2f%%]" % ((n+1)/10.95))
sys.stdout.flush()
sys.stdout.write("\n")
if os.path.exists("./sim_matrix.dat"):
f.close()
else:
f = open("sim_matrix.dat","w")
for c in C:
f.write(" ".join(str(v) for v in c))
f.write("\n")
f.close()
merge_index=[]
#print C
sys.stdout.write("\rclustering...[%.2f%%]" % (0))
sys.stdout.flush()
for k in xrange(0,N-K):
pair = argmax(C)
cluster[pair[0]].append(pair[1])
for j in xrange(0,N):
val = cluster_sim(cluster[j], cluster[pair[0]], C)
C[pair[0]][j] = val
C[j][pair[0]] = val
sys.stdout.write("\rclustering...[%.2f%%]" % (float(k*N + j + 1)*100/(N*(N-K))))
sys.stdout.flush()
I[pair[1]] = 0
merge_index.append(pair[1])
sys.stdout.write("\n")
print "----- save in the file -----"
save = open(str(K)+".txt","w")
for index in cluster:
if index not in merge_index:
for docid in cluster[index]:
save.write(str(docid+1))
save.write("\n")
save.write("\n")
save.close()
print "finish clustering ^^"
