def Search(self):
profiles = set()
medoids_clusters = {}
# 对每个领域聚类
people = datapre.People(self.features)
for category in self.categories.keys():
# 对每个领域进行聚类
number = int(self.k * self.categories[category]) + 1
tuples = people[category]
method = KMedoidsCluster(
number, datapre.FeaturesById(tuples, self.features), category)
clusters, medoids = method.Cluster()
# 先加入到profiles中
for medoid in medoids:
profiles.add(medoid)
medoids_clusters[medoid] = clusters[medoid]
print "开始删除"
# 删除多出来的
profiles = self.Delete(profiles)
print "开始替换"
profiles = self.Replace(profiles, medoids_clusters)
return profiles
def Replace(self, profiles, cluster):
'''
:param profiles: 完成的中心点
:param cluster: 字典形式的,以profiles为key,聚类簇value为列表格式
:return: 返回替换好的profiles
'''
# 替换过程用离medoids最近的且满足要求的元素来替换
while True:
iteration = True
new_profiles = deepcopy(profiles)
for profile in profiles:
if not metric.checkOneTypical(self.features, profile,
new_profiles, self.epsilon):
new_profiles.remove(profile)
# 对profile进行替换,在cluster[profile]寻找profile对其代表性最大的元素,且满足条件的来替换
R = np.load("new%sRepresentativeMatrix.npy" %
self.features[profile][5])
# 加载id字典
open_file = open("new%sRepresentativeDictionary.pickle" %
self.features[profile][5])
R_dic = pickle.load(open_file)
open_file.close()
# 在其聚类簇中寻找到其代表性最大的来替换
results = {
id: R[R_dic[id]][R_dic[profile]]
for id in cluster[profile]
}
# results = {element:metric.Repre(self.features[profile],self.features[element]) for element in cluster[profile]}
results = sorted(results.items(),
key=lambda key: key[1],
reverse=True)
flag = False
# 在results中找到profile最能代表的,且满足领域典型要求的元素
for result in results:
key = result[0]
if metric.checkOneTypical(self.features, key,
new_profiles, self.epsilon):
new_profiles.add(key)
cluster[key] = cluster[profile]
cluster.pop(profile)
flag = True
break
# 没找到领域典型的,需要在该领域的原集中去除这部分元素,重新聚类
if flag == False:
iteration = False
# 对该领域去除这部分元素后,重新寻找k个聚类簇
category = self.features[profiles][5]
for profile in profiles:
if self.features[profile][5] == category:
new_profiles.remove(profile)
# 获取该领域的人物集合
tuples = datapre.People(self.features)[category]
# 去除cluster[profile]这部分元素
for element in tuples:
if element in set(cluster[profile]):
tuples.remove(element)
number = 0
for profile in profiles:
if self.features[profile][5] == category:
number += 1
# 重新对tuples聚类
method = KMedoidsCluster(
number,
datapre.FeaturesById(tuples,
self.features), category)
clusters, medoids = method.Cluster()
for key in clusters.keys():
cluster[key] = clusters[key]
for element in medoids:
new_profiles.add(element)
# 此时new_profiles是最新的,继续向下替换
if iteration == True:
break
else:
profiles = new_profiles
return new_profiles
