def Run():
features = datapre.Features()
epsilons = [0.1560, 0.1556, 0.1555]
number = [40, 60, 80, 100]
for epsilon in epsilons:
for n in number:
start_time = time.time()
profile1 = greedy.Greedy(n, features,
datapre.CategoriesDistribution(),
epsilon).SearchWithReplace()
end_time = time.time()
with open("GB%d_%.4f" % (n, epsilon), "wb") as f:
f.write("cost %f s" % (end_time - start_time))
f.write("Attribute Representativeness is:")
f.write(str(metric.AttributeRepresentative(features,
profile1)))
f.write("\n")
for profile in profile1:
f.write(profile + "\t")
start_time = time.time()
profile2 = kmedoids.KMedoids(n, features,
datapre.CategoriesDistribution(),
epsilon).Search()
end_time = time.time()
with open("kmedoids%d_%.4f" % (n, epsilon), "wb") as f:
f.write("cost %f s" % (end_time - start_time))
f.write("Attribute Representativeness is:")
f.write(str(metric.AttributeRepresentative(features,
profile2)))
f.write("\n")
for profile in profile2:
f.write(profile + "\t")
start_time = time.time()
profile3 = sa.SAalgo(n, features, datapre.CategoriesDistribution(),
epsilon, 0.3, 10, 0.9).Search()
end_time = time.time()
with open("sa%d_%.4f" % (n, epsilon), "wb") as f:
f.write("cost %f s" % (end_time - start_time))
f.write("Attribute Representativeness is:")
f.write(str(metric.AttributeRepresentative(features,
profile3)))
f.write("\n")
for profile in profile3:
f.write(profile + "\t")
def test():
features = datapre.Features()
to_run = [40, 60, 80, 100]
for i in to_run:
start_time = time.time()
method = KMedoids(i, datapre.Features(),
datapre.CategoriesDistribution(), 0.1555)
profiles = method.Search()
end_time = time.time()
print metric.AttributeRepresentative(features, profiles)
print profiles
print "cost %f s" % (end_time - start_time)
with open("%dclustering_result" % i, "wb") as f:
f.write("cost %f s" % (end_time - start_time))
f.write("\n")
f.write("Attribute Representativeness is:")
f.write(str(metric.AttributeRepresentative(features, profiles)))
f.write("\n")
for profile in profiles:
f.write(profile + "\t")
# test()
