def compute_updating_relation(self, obj):
list_rela = []
# print("新个人数据网络的实例个数：", len(self.list_network))
for i in range(len(self.list_network)):
iter_obj = self.list_network[i] # 迭代的对象
original_content = obj.get_content()
if (original_content == "") or (original_content is None):
original_content = str(obj.get_keywords())
if obj is iter_obj:
continue
iterate_content = iter_obj.get_content() + iter_obj.get_title()
if (iterate_content == "") or (iterate_content is None):
iterate_content = str(iter_obj.get_keywords())
# print("original_content:", original_content)
# print("iter_cont:", iterate_content)
# 计算两个不同文本的相似性
rate = Levenshtein.ratio(original_content, iterate_content)
# print("rate:", rate)
# 判断如果相似率大于阈值，则存入个人数据网络中
if rate >= self.ratio:
# print("rate:", rate)
# obj新插入的对象； iter_obj是老对象
# print("新对象对应的Class:", obj.__class__.__name__,
# ", type:", type(obj.__class__.__name__))
# 获取联系
relation = cm.get_relation(iter_obj.__class__, obj.__class__)
# 存入初始个人数据网络，字典数据结构
dict_relation = {
"pre_id": iter_obj.get_id(),
"relation": relation,
"post_id": obj.get_id(),
"pre_Class": iter_obj.__class__.__name__,
"post_Class": obj.__class__.__name__,
"pre_Activity": iter_obj.get_activity(),
"post_Activity": obj.get_activity(),
"pre_service": iter_obj.get_service(),
"pre_title": iter_obj.get_title(),
"pre_content": iter_obj.get_content(),
"post_service": obj.get_service(),
"post_title": obj.get_title(),
"post_content": obj.get_content()
}
if relation != "Update":
print("relation:", dict_relation)
# 将每一个新联系加载到队列中
# print("新联系:", dict_relation)
list_rela.append(dict_relation)
return list_rela
def initial_data_status(self):
counter = 0 # 计数器
# 查询所有记录
cursors = self.collection.find()
for data in cursors:
# 大于初始数据网络的一条数据处理
if counter >= self.k_no:
break
obj = self.create_class_obj(data)
self.list_network.append(obj)
counter += 1 # 计数器
print("counter:", counter)
# 查找联系
if counter >= 2:
for i in range(len(self.list_network)):
iter_obj = self.list_network[i] # 迭代的对象
original_content = obj.get_content()
if (original_content == "") or (original_content is None):
original_content = str(obj.get_keywords())
if obj is iter_obj:
continue
iterate_content = iter_obj.get_content() + \
iter_obj.get_title()
if (iterate_content == "") or (iterate_content is None):
iterate_content = str(iter_obj.get_keywords())
print("original_content:", original_content)
print("iter_cont:", iterate_content)
# 计算两个不同文本的相似性
rate = Levenshtein.ratio(original_content, iterate_content)
print("rate:", rate)
# 判断如果相似率大于阈值，则存入个人数据网络中
if rate >= self.ratio:
# obj新插入的对象； iter_obj是老对象
# print("新对象对应的Class:", obj.__class__.__name__,
# ", type:", type(obj.__class__.__name__))
# 获取联系
relation = cm.get_relation(iter_obj.__class__,
obj.__class__)
# 存入初始个人数据网络，字典数据结构
dict_relation = {
"pre_id": iter_obj.get_id(),
"relation": relation,
"post_id": obj.get_id(),
"pre_Class": iter_obj.__class__.__name__,
"post_Class": obj.__class__.__name__,
"pre_Activity": iter_obj.get_activity(),
"post_Activity": obj.get_activity()
}
self.list_network_relation.append(dict_relation)
# self.list_network.extend(self.list_network_relation)
if self.list_network:
for i in range(len(self.list_network)):
print("i:", i, ", list_network:", self.list_network[i])
else:
print("list_network is empty!")
if self.list_network_relation:
for i in range(len(self.list_network_relation)):
print("i:", i, ", list_network:",
self.list_network_relation[i])
else:
print("list_network_relation is empty!")
print("counter:", counter)
def initial_data_status(self):
counter = 0 # 计数器
# 查询所有记录
cursors = self.collection.find().skip(self.kip)
for data in cursors:
# 大于初始数据网络的一条数据处理
if counter >= self.k_no:
break
# 设置Linkedin用户状态数据的初始状态
if counter == self.k_no - 1:
# print("最后一条记录！！")
self.flag = self.initialize_linked_status(data)
print("service:", data.get("服务ID"))
obj = self.create_class_obj(data) # 创建对象
self.list_network.append(obj)
counter += 1 # 计数器
print("counter:", counter)
# 查找联系
if counter >= 2:
for i in range(len(self.list_network)):
iter_obj = self.list_network[i] # 迭代的对象
original_content = obj.get_content()
if (original_content == "") or (original_content is None):
original_content = str(obj.get_keywords())
if obj is iter_obj:
continue
iterate_content = iter_obj.get_content() + \
iter_obj.get_title()
if (iterate_content == "") or (iterate_content is None):
iterate_content = str(iter_obj.get_keywords())
# print("original_content:", original_content)
# print("iter_cont:", iterate_content)
# 计算两个不同文本的相似性
rate = Levenshtein.ratio(original_content, iterate_content)
# print("rate:", rate)
# 判断如果相似率大于阈值，则存入个人数据网络中
if rate >= self.ratio:
# obj新插入的对象； iter_obj是老对象
# print("新对象对应的Class:", obj.__class__.__name__,
# ", type:", type(obj.__class__.__name__))
# 获取联系
relation = cm.get_relation(iter_obj.__class__,
obj.__class__)
# 存入初始个人数据网络，字典数据结构
dict_relation = {
"pre_id": iter_obj.get_id(),
"relation": relation,
"post_id": obj.get_id(),
"pre_Class": iter_obj.__class__.__name__,
"post_Class": obj.__class__.__name__,
"pre_Activity": iter_obj.get_activity(),
"post_Activity": obj.get_activity(),
"pre_service": iter_obj.get_service(),
"pre_content": iter_obj.get_content(),
"pre_title": iter_obj.get_title(),
"post_service": obj.get_service(),
"post_content": obj.get_content(),
"post_title": obj.get_title()
}
self.list_network_relation.append(dict_relation)
# self.list_network.extend(self.list_network_relation)
# 1.节点
print("节点：")
if self.list_network:
for i in range(len(self.list_network)):
print("i:", i, ", list_network:", self.list_network[i])
else:
print("list_network is empty!")
# 2.联系
print("联系")
if self.list_network_relation:
for i in range(len(self.list_network_relation)):
print("i:", i, ", list_network:",
self.list_network_relation[i])
else:
print("list_network_relation is empty!")
# 3.输出用户职位数据 linkedin
if self.position:
for i in range(len(self.position)):
print("i:", ", position:", self.position[i])
else:
print("list_position is Empty!")
print("counter:", counter)