residual = [data2[i]-y_true[i] for i in range(500)] plt.scatter(data2[:500],residual) plt.show()这段代码中加入一条y=0.1*x的直线

若要在残差图中添加一条直线 `y = 0.1*x`，可以在绘制散点图之后，再调用 `plt.plot()` 函数来添加直线的绘制。

以下是修改后的代码示例：

import numpy as np
import matplotlib.pyplot as plt

# 假设 data2 和 y_true 是已经定义好的数据

residual = [data2[i] - y_true[i] for i in range(500)]

plt.scatter(data2[:500], residual)
plt.plot(data2[:500], 0.1 * data2[:500], color='r', linestyle='--')  # 添加直线 y = 0.1*x
plt.xlabel('data2')
plt.ylabel('residual')
plt.title('Residual Plot')
plt.show()

在修改后的代码中，我们在 `plt.scatter()` 之后调用了 `plt.plot()` 函数，并传入 `data2[:500]` 作为横轴数据，`0.1 * data2[:500]` 作为纵轴数据，设置 `color='r'` 表示红色线条，`linestyle='--'` 表示虚线样式。

这样就在残差图上添加了一条直线 `y = 0.1*x`，可以更直观地观察到残差的分布情况与直线之间的关系。

相关问题

residual = [data2[i]-y_true[i] for i in range(500)] plt.scatter(data2[:500],residual) plt.show()

这段代码是用于绘制残差图的示例代码。假设`data2`是一个包含500个样本的数据集，`y_true`是对应的真实目标变量值。

代码中首先计算了残差，即每个样本的观测值与对应的真实值之间的差异。然后使用`plt.scatter`函数将`data2[:500]`作为横轴，`residual`作为纵轴，绘制散点图。散点图上的点表示每个样本的预测值与真实值之间的残差。

最后使用`plt.show()`显示图形。

请注意，这只是一个简单的示例代码，具体的实现可能需要根据您的数据集和需求进行调整和修改。

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]