Y=[] for ind in review_long_clean.index_content.unique(): y=[ word for word in review_long_clean.content_type[review_long_clean.index_content==ind].unique() ] Y.append(y) len(Y) X=[] for ind in review_long_clean.index_content.unique(): term=[ word for word in review_long_clean.word[review_long_clean.index_content==ind].values ] X.append(' '.join(term)) len(X) X Y

根据你提供的代码，我可以看出这是一个数据处理的过程，其中 X 和 Y 分别是特征和标签。其中 X 是一个列表，每个元素是一篇文本的词语拼接而成的字符串，Y 是一个列表，每个元素是一篇文本的标签列表。具体而言，代码中使用了 pandas 库进行数据处理，从 review_long_clean 数据集中根据 index_content 列进行了分组，然后按照 content_type 列和 word 列分别提取了标签和词语，并将它们组成了 X 和 Y 两个列表。最后，X 和 Y 的长度应该是相等的，分别对应了数据集中 unique 的 index_content 的数量。

相关问题

import torch import torch.nn.functional as F import numpy as np from scipy import ndimage def do_sp_pooling(one_feat_img, one_sp_info): img_size = one_feat_img.shape num_units = img_size[0] * img_size[1] dim = img_size[2] one_feat_img = one_feat_img.reshape(num_units, dim) img_size_org = one_sp_info['img_size'] pixel_ind_map = np.arange(num_units).reshape(img_size[0], img_size[1]) pixel_ind_map_org = ndimage.zoom(pixel_ind_map, [img_size_org[0]/img_size[0], img_size_org[1]/img_size[1]], order=0) pixel_ind_sps = one_sp_info['pixel_ind_sps'] num_sp = len(pixel_ind_sps) weight_pool_info = torch.zeros((num_sp, num_units), dtype=one_feat_img.dtype, device=one_feat_img.device) for idx_sp in range(num_sp): pixel_ind_sp_one = pixel_ind_sps[idx_sp] ind_pixels_in_map = pixel_ind_map_org[pixel_ind_sp_one] _, uniqueIndex = np.unique(ind_pixels_in_map, return_inverse=True) frequency = np.bincount(uniqueIndex) / len(ind_pixels_in_map) frequency = frequency.astype(one_feat_img.dtype) freq_one_sp = torch.zeros(num_units, dtype=one_feat_img.dtype, device=one_feat_img.device) freq_one_sp[ind_pixels_in_map] = torch.tensor(frequency, dtype=one_feat_img.dtype, device=one_feat_img.device) weight_pool_info[idx_sp, :] = freq_one_sp one_feat_sps = torch.mm(weight_pool_info, one_feat_img) return one_feat_sps, weight_pool_info，根据上述代码，给出一个详尽的流程

这段代码实现了SP（super-pixel）Pooling的过程，其流程如下：

1.导入必要的库：torch，numpy和scipy中的ndimage。

2.定义一个名为do_sp_pooling的函数，该函数接收两个参数：一个特征图和一个super-pixel信息字典。

3.获取特征图的大小（img_size）和维度（dim），并将特征图重新调整为num_units * dim的形状。

4.获取原始图像的大小（img_size_org）和像素索引映射（pixel_ind_map_org），并使用ndimage.zoom函数将像素索引映射重新调整为原始图像的大小。

5.获取super-pixel信息中的像素索引（pixel_ind_sps）和super-pixel数量（num_sp）。

6.创建一个形状为num_sp * num_units的零张量weight_pool_info，用于存储每个super-pixel对应的像素的权重信息。

7.对于每个super-pixel：

a.获取该super-pixel的像素索引（pixel_ind_sp_one）。

b.使用像素索引映射获取每个像素在像素索引映射中的索引（ind_pixels_in_map）。

c.使用np.unique函数计算ind_pixels_in_map中的唯一值，并返回这些唯一值和它们在ind_pixels_in_map中的索引（uniqueIndex）。

d.使用np.bincount函数计算uniqueIndex中每个唯一值出现的频率，并将其除以ind_pixels_in_map的长度。

e.创建一个零张量freq_one_sp，其形状为num_units，用于存储当前super-pixel的像素在特征图中的权重信息。

f.将频率信息转换为张量，并将其存储到freq_one_sp中，使用ind_pixels_in_map作为索引。

g.将freq_one_sp存储到weight_pool_info的当前super-pixel行中。

8.将weight_pool_info与one_feat_img相乘，得到每个super-pixel的特征表示one_feat_sps。

9.返回one_feat_sps和weight_pool_info。

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]