] * 10 #stores the sse metric for each number of clusters from 5 to 50
num_users = 100
numsse = 0
numclusters = 5 # starts at 5
max_iterations = 10
start_time = datetime.datetime.now()
while numclusters <= 50: # compute SSE from num_clusters=5 to 50
users = [] # users are the items of this example
for i in range(num_users):
user = createProfile()
users.append(user)
print(" inicializing kmeans...")
cl = KMeansClustering(users, HDdistItems, HDequals)
print(" executing...", numclusters)
st = datetime.datetime.now()
print(st)
numclusters = numclusters
solution = cl.HDgetclusters(numclusters, max_iterations)
for i in range(numclusters):
a = solution[i]
print(util.HDcentroid(a), ",")
st = datetime.datetime.now()
sses[numsse] = HDcomputeSSE(solution, numclusters)
numsse += 1
numclusters += 5
end_time = datetime.datetime.now()
print("start_time:", start_time)
print("end_time:", end_time)
print("sses:", sses)
start_time = datetime.datetime.fromtimestamp(ts).strftime('%Y-%m-%d %H:%M:%S')
while numclusters <= 50: # compute SSE from num_clusters=5 to 50
supersol = 0 #supersolucion, distancias entre el clusters y los usuarios.
users = [] # users are the items of this example
for i in range(num_users): #en el range el numero de usuarios
user = createProfile()
users.append(user)
#x=0;
print " inicializing kmeans..."
cl = KMeansClustering(users, HDdistItems, HDequals)
print " executing...", numclusters
ts = time.time()
st = datetime.datetime.fromtimestamp(ts).strftime('%Y-%m-%d %H:%M:%S')
print st
numclusters = numclusters
solution = cl.HDgetclusters(numclusters, max_iteraciones)
for i in range(numclusters):
a = solution[i]
print util.HDcentroid(a), ","
ts = time.time()
st = datetime.datetime.fromtimestamp(ts).strftime('%Y-%m-%d %H:%M:%S')
sses[numsse] = HDcomputeSSE(solution, numclusters)
numsse += 1
numclusters += 5
ts = time.time()
end_time = datetime.datetime.fromtimestamp(ts).strftime('%Y-%m-%d %H:%M:%S')
print "start_time:", start_time
print "end_time:", end_time
print "sses:", sses
