import ckmeans
import random
import time

# print dir(ckmeans)
# print dir(ckmeans.k_means)

N = 1000000
K = 50

lat = [random.randint(1000, 2000)/100. for _ in xrange(N)]
lon = [random.randint(4000, 5000)/100. for _ in xrange(N)]


print "Version:", ckmeans.version(), "\n"

s_time = time.time()
print "Start clustering ... ",
ans = ckmeans.k_means(lat, lon, K)
print "DONE"
# print "ANS:", ans
print "Time to cluster {0} points in to {1} clusters was: {2} seconds".format(N, K, (time.time() - s_time))

import ckmeans
import random
import time

# print dir(ckmeans)
# print dir(ckmeans.k_means)

N = 1000000
K = 50

lat = [random.randint(1000, 2000)/100. for _ in xrange(N)]
lon = [random.randint(4000, 5000)/100. for _ in xrange(N)]


print "Version:", ckmeans.version(), "\n"

s_time = time.time()
print "Start clustering ... ",
ans = ckmeans.k_means(lat, lon, K)
print "DONE"
# print "ANS:", ans
print "Time to cluster {0} points in to {1} clusters was: {2} seconds".format(N, K, (time.time() - s_time))

# print dir(ckmeans) # print dir(ckmeans.k_means) # print "ANS:", ans