query_order = "MATCH (o:Order) RETURN o" result_order = graph.run(query_order)
时间: 2024-03-15 18:42:03 浏览: 18
这段代码使用了 py2neo 中的 Graph 对象的 "run" 方法来运行一个 Cypher 查询语句。这个查询语句是 "MATCH (o:Order) RETURN o",意思是查询所有标签为 "Order" 的节点,并返回它们。"result_order" 是一个 py2neo.RecordList 类型的对象,它包含了所有查询结果。
你可以通过 "result_order" 对象来访问这些查询结果。例如,你可以使用 "result_order.data()" 方法来获取一个包含所有查询结果的列表。如果你只想获取第一个查询结果,可以使用 "result_order.evaluate()" 方法。你还可以使用其他方法来操作查询结果,这些方法可以在 py2neo 的文档中找到。
相关问题
for record_order in result_order: node_order = record_order['o'] customerID_value = node_order['customerID'] query_customer_id = "MATCH (u:Customer) WHERE u.customerID = $customer_id RETURN u" result_customer_id = graph.run(query_customer_id, parameters={"customer_id": f"{customerID_value}"}) for record_customer in result_customer_id: node_customer = record_customer['u'] relationship = Relationship(node_customer, customer_order_type, node_order) graph.create(relationship)
这段代码使用了查询结果 "result_order" 中的所有节点,并针对每个节点执行一些操作。具体来说,它查找了每个 "Order" 节点的 "customerID" 属性,并将其用作参数来查询匹配的 "Customer" 节点。然后,它使用 "Relationship" 类创建了一个在 "Customer" 节点和 "Order" 节点之间的关系,并使用 "graph.create" 方法在数据库中创建了这个关系。
这个循环的作用是将 "Customer" 节点与 "Order" 节点连接起来,以表示它们之间的关系。这种关系可以用来查询特定客户的订单历史记录,或者查询特定订单的客户信息。
class AbstractGreedyAndPrune(): def __init__(self, aoi: AoI, uavs_tours: dict, max_rounds: int, debug: bool = True): self.aoi = aoi self.max_rounds = max_rounds self.debug = debug self.graph = aoi.graph self.nnodes = self.aoi.n_targets self.uavs = list(uavs_tours.keys()) self.nuavs = len(self.uavs) self.uavs_tours = {i: uavs_tours[self.uavs[i]] for i in range(self.nuavs)} self.__check_depots() self.reachable_points = self.__reachable_points() def __pruning(self, mr_solution: MultiRoundSolution) -> MultiRoundSolution: return utility.pruning_multiroundsolution(mr_solution) def solution(self) -> MultiRoundSolution: mrs_builder = MultiRoundSolutionBuilder(self.aoi) for uav in self.uavs: mrs_builder.add_drone(uav) residual_ntours_to_assign = {i : self.max_rounds for i in range(self.nuavs)} tour_to_assign = self.max_rounds * self.nuavs visited_points = set() while not self.greedy_stop_condition(visited_points, tour_to_assign): itd_uav, ind_tour = self.local_optimal_choice(visited_points, residual_ntours_to_assign) residual_ntours_to_assign[itd_uav] -= 1 tour_to_assign -= 1 opt_tour = self.uavs_tours[itd_uav][ind_tour] visited_points |= set(opt_tour.targets_indexes) # update visited points mrs_builder.append_tour(self.uavs[itd_uav], opt_tour) return self.__pruning(mrs_builder.build()) class CumulativeGreedyCoverage(AbstractGreedyAndPrune): choice_dict = {} for ind_uav in range(self.nuavs): uav_residual_rounds = residual_ntours_to_assign[ind_uav] if uav_residual_rounds > 0: uav_tours = self.uavs_tours[ind_uav] for ind_tour in range(len(uav_tours)): tour = uav_tours[ind_tour] quality_tour = self.evaluate_tour(tour, uav_residual_rounds, visited_points) choice_dict[quality_tour] = (ind_uav, ind_tour) best_value = max(choice_dict, key=int) return choice_dict[best_value] def evaluate_tour(self, tour : Tour, round_count : int, visited_points : set): new_points = (set(tour.targets_indexes) - visited_points) return round_count * len(new_points) 如何改写上述程序,使其能返回所有已经探索过的目标点visited_points的数量,请用代码表示
可以在 `solution()` 方法中添加一个变量来记录已经探索过的目标点数量,然后在每次更新 `visited_points` 后更新这个变量。下面是修改后的代码:
```
class AbstractGreedyAndPrune():
def __init__(self, aoi: AoI, uavs_tours: dict, max_rounds: int, debug: bool = True):
self.aoi = aoi
self.max_rounds = max_rounds
self.debug = debug
self.graph = aoi.graph
self.nnodes = self.aoi.n_targets
self.uavs = list(uavs_tours.keys())
self.nuavs = len(self.uavs)
self.uavs_tours = {i: uavs_tours[self.uavs[i]] for i in range(self.nuavs)}
self.__check_depots()
self.reachable_points = self.__reachable_points()
def __pruning(self, mr_solution: MultiRoundSolution) -> MultiRoundSolution:
return utility.pruning_multiroundsolution(mr_solution)
def solution(self) -> Tuple[MultiRoundSolution, int]:
mrs_builder = MultiRoundSolutionBuilder(self.aoi)
for uav in self.uavs:
mrs_builder.add_drone(uav)
residual_ntours_to_assign = {i : self.max_rounds for i in range(self.nuavs)}
tour_to_assign = self.max_rounds * self.nuavs
visited_points = set()
explored_points = 0
while not self.greedy_stop_condition(visited_points, tour_to_assign):
itd_uav, ind_tour = self.local_optimal_choice(visited_points, residual_ntours_to_assign)
residual_ntours_to_assign[itd_uav] -= 1
tour_to_assign -= 1
opt_tour = self.uavs_tours[itd_uav][ind_tour]
new_points = set(opt_tour.targets_indexes) - visited_points
explored_points += len(new_points)
visited_points |= new_points # update visited points
mrs_builder.append_tour(self.uavs[itd_uav], opt_tour)
return self.__pruning(mrs_builder.build()), explored_points
class CumulativeGreedyCoverage(AbstractGreedyAndPrune):
def evaluate_tour(self, tour : Tour, round_count : int, visited_points : set):
new_points = set(tour.targets_indexes) - visited_points
return round_count * len(new_points)
def local_optimal_choice(self, visited_points, residual_ntours_to_assign):
choice_dict = {}
for ind_uav in range(self.nuavs):
uav_residual_rounds = residual_ntours_to_assign[ind_uav]
if uav_residual_rounds > 0:
uav_tours = self.uavs_tours[ind_uav]
for ind_tour in range(len(uav_tours)):
tour = uav_tours[ind_tour]
quality_tour = self.evaluate_tour(tour, uav_residual_rounds, visited_points)
choice_dict[quality_tour] = (ind_uav, ind_tour)
best_value = max(choice_dict, key=int)
return choice_dict[best_value]