def kmeans_centroids(self, clusters):
'''Calculate centroids based on clusters by doing a mean between
all states within the same cluster.
'''
Helper.verbose_print('Centroids: ' + str(self.get_centroids_count(clusters)))
new_centroids = [0 for _ in range(self.k)]
for i in range(self.k):
state_sum = tuple([(0,0) for _ in range(self.coord_nr)])
nr = 0.0
for state,cluster in clusters.iteritems():
if cluster == i:
nr += 1
state_sum = self.add_states(state_sum, state)
# At least one representat for a cluster should exist.
if nr > 0:
# Now divide by the number of members in a cluster every coordinate.
new_centroids[i] = map(lambda coord: self.__divide_coord(coord, nr),
state_sum)
# Treat the case of finding no member in cluster by making it be
# infinity everywhere.
else:
new_centroids[i] = map(lambda coord: tuple([Preprocess.INFINITY
for c in coord]),
state_sum)
return new_centroids
