def reduce_pareto_set(self, par_set):
"""
Realiza el clustering
"""
lista_cluster=[]
for solucion in par_set.solutions:
cluster = Cluster()
cluster.agregar_solucion(solucion)
lista_cluster.append(cluster)
while len(lista_cluster) > self.max_pareto_points:
min_distancia = sys.maxint
for i in range (0,len(lista_cluster)-1):
for j in range(i+1, len(lista_cluster)-1):
c = lista_cluster[i]
distancia = c.calcular_distancia(lista_cluster[j])
if distancia < min_distancia:
min_distancia = distancia
c1 = i
c2 = j
cluster = lista_cluster[c1].unir(lista_cluster[c2]) #retorna un nuevo cluster
del lista_cluster[c1]
del lista_cluster[c2]
lista_cluster.append(cluster)
par_set=[]
for cluster in lista_cluster:
solucion = cluster.centroide()
par_set.append(solucion)
return par_set
def get_cluster_obs(clusters, r):
res = Cluster(-1, [])
for c in clusters:
for obs_row in c.observations:
if obs_row == r:
res.id = c.id
res.centroide = c.centroide
res.observations = c.observations
return res
def get_cluster_obs(clusters, r): res = Cluster(-1, []) for c in clusters: for obs_row in c.observations: if obs_row == r: res.id = c.id res.centroide = c.centroide res.observations = c.observations return res
