解释如下代码： length_list = [list(range(32, 1025, 16)) + list(range(1056, 8193, 16))] width_list = [list(range(16, 145, 1))] length_max = max(length_list[0]) width_max = max(width_list[0]) def cut_rectangle(length, width): if length > length_max and width > width_max: rectangles = [] a_length = length_max b_length = length - length_max a_rectangle = (a_length, width) b_rectangle = (b_length, width) if b_length > length_max: a_rectangles, b_rectangles = cut_rectangle(b_length, width) rectangles.extend(a_rectangles) rectangles.extend(b_rectangles) else: rectangles.append(b_rectangle) if a_length > width_max: new_a_rectangles = [a_rectangle] while new_a_rectangles: a_rectangles = new_a_rectangles new_a_rectangles = [] for rectangle in a_rectangles: a_width = rectangle[1] if a_width > width_max: half_width = math.ceil(a_width / 2) if half_width > width_max: new_a_rectangle = (a_length, half_width) b_length = rectangle[0] b_rectangle = (b_length, a_width - half_width) if b_length > length_max: a_rectangles, b_rectangles = cut_rectangle(b_length, a_width - half_width) rectangles.extend(a_rectangles) rectangles.extend(b_rectangles) else: rectangles.append(b_rectangle) new_a_rectangles.append(new_a_rectangle) else: new_a_rectangles.append(rectangle) else: rectangles.append(rectangle) else: rectangles.append(a_rectangle) return rectangles, [] else: return [(length, width)], [] length = int(input("请输入被切割矩形的长度值：")) width = int(input("请输入被切割矩形的宽度值：")) rectangles, _ = cut_rectangle(length, width) print("全部切割后的矩形尺寸的列表：") for rectangle in rectangles: print(f"{rectangle[0]} x {rectangle[1]}")

Python列表操作详解与示例

"本文主要介绍了Python编程语言中的列表（list）操作，包括基本操作和一些高级技巧。列表作为Python的基本数据结构，具有动态增长的特点，能够容纳不同类型的数据。" 在Python中，列表是一种非常重要的数据结构，它...

Java Stream API：10个顶级优化技巧，提升代码效率

List<Integer> numbers = IntStream.range(1, 1000000).boxed().collect(Collectors.toList()); long sum = numbers.parallelStream().mapToInt(Integer::intValue).sum(); 4. 用Collectors.groupingBy()进行...

解释如下代码： length_list = [list(range(32, 1025, 16)) + list(range(1056, 8193, 16))] width_list = [list(range(16, 145, 1))] length_max = max(length_list[0]) width_max = max(width_list[0]) def cut_rectangle(length, width): if length > length_max and width > width_max: rectangles = [] a_length = length_max b_length = length - length_max a_rectangle = (a_length, width) b_rectangle = (b_length, width) if b_length > length_max: a_rectangles, b_rectangles = cut_rectangle(b_length, width) rectangles.extend(a_rectangles) rectangles.extend(b_rectangles) else: rectangles.append(b_rectangle) if a_length > width_max: new_a_rectangles = [a_rectangle]

这段代码定义了两个列表 length_list 和 width_list，分别存储了一些长度和宽度的取值范围。然后通过 max 函数找到了这些取值范围中的最大值 length_max 和 width_max。最后定义了一个函数 cut_rectangle，用于将一...

解释def extractFlow(flow_list): session_list = [] for idx in range(len(flow_list)): tuple_list = [row for row in flow_list] # 复制整个列表，得到一个元组列表 five_tuple_list = [t[0] for t in tuple_list] # 提取每个元组的第一个元素 length_list_list = [t[1] for t in tuple_list] # 提取每个元组的第二个元素 # 将长度列表转换为长度序列 length_seq_list = [[len_list[i] for i in range(len(len_list))] for len_list in length_list_list] tuple_list = [row for row in session_list] # 复制整个列表，得到一个元组列表 five_tuple_list = [t[0] for t in tuple_list] # 提取每个元组的第一个元素 length_list_list = [t[1] for t in tuple_list] # 提取每个元组的第二个元素 # 将长度列表转换为长度序列 length_seq_list = [[len_list[i] for i in range(len(len_list))] for len_list in length_list_list] return session_list

length_seq = [len_list[i] for i in range(len(len_list))] # 判断该流是否属于已有的会话 is_in_session = False for session_idx in range(len(session_list)): if five_tuple == session_list[session_...

分析class MemoryManager: def init(self, allocator): self.allocator = allocator self.process_list = [] def allocate(self, process, request_size): block_start = -1 end = -1 memory_view = self.allocator.memory_view() i = 0 while i < 256: if memory_view[i] == None: if end < 0: end = i if (memory_view[i] is not None) and (end >= 0): move_lenght = i - end k = memory_view[i] for j in self.process_list: if j == k: a = j.get_memory() j_start = a[0] self.allocator.free_memory(j) length_area = j.block j_start = j_start - move_lenght self.allocator.allocate_memory(j_start, length_area, j) memory_view = self.allocator.memory_view() i = -1 break end = -1 i = i + 1 memory_view = self.allocator.memory_view() for j in range(len(memory_view)): # 0~255 if memory_view[j] == None: if block_start < 0: block_start = j if (memory_view[j] is not None) and (block_start >= 0): if j - block_start == request_size: break elif j - block_start < request_size: block_start = -1 self.process_list.append(process) self.allocator.allocate_memory(block_start, request_size, process)

而process_list是一个存储进程的列表，初始为空。allocate函数接受一个进程process和请求的内存大小request_size作为输入参数，然后通过访问分配器的memory_view方法来获取内存的使用情况，并根据情况进行内存分配。...

将这段python代码转化为java :ef six2Json(result): res_json = dict() try: res_list = result.split("\r\n") for index in range(len(res_list) - 1): if index % 2 == 0: res_json[res_list[index]] = res_list[index + 1] except: pass return res_json

以下是将该 Python 代码转换为 Java 的等效代码： java import java.util.HashMap; public class Main { public static HashMap, String> six2Json(String result) { HashMap, String> resJson = new HashMap...

解释一下下面的代码：def get_time_length(date,max_waiting_time=360): time_length={} start=time.time() if date=='weekday': for i in range(24): wd_hour='wd_'+str(i) time_length[i]=dict(nx.all_pairs_dijkstra_path_length(G,cutoff=max_waiting_time,weight=wd_hour)) elif date=='weekend': for i in range(24): we_hour='we_'+str(i) time_length[i]=dict(nx.all_pairs_dijkstra_path_length(G,cutoff=max_waiting_time,weight=we_hour)) else: print('date input error') end=time.time() print(end-start) gc.collect() start=time.time() new_time_length={} for i in range(24): new_time_length[i]={} for j in nodes_list: new_time_length[i][j]={} for i in range(24): for j in nodes_list: for p,value in time_length[i][j].items(): new_time_length[i][p][j]=value print(i) end=time.time() print(end-start) return new_time_length

这段代码定义了一个函数 get_time_length，它接受两个参数：date 和 max_waiting_time。函数首先创建一个空字典 time_length 和一个计时器 start，然后根据 date 的值，分别对每个小时进行遍历，计算出每个节点之间...

帮我解释一下这段代码def select(generation, value_min, value_max, accuracy): fitness_list = [get_fitness(DNA, value_min, value_max, accuracy) for DNA in generation] p_list = [f / sum(fitness_list) for f in fitness_list] idx = list(range(len(generation))) idx_selected = np.random.choice(idx, size=len(generation), replace=True, p=p_list) # 根据概率选择，有放回抽样 return [generation[i] for i in idx_selected] def crossover(generation, DNA_length, crossover_probability): template = [np.random.rand() <= crossover_probability for i in range(DNA_length)] new_generation = [] for i in range(0, len(generation) - 1, 2): DNA_a = generation[i] DNA_b = generation[i + 1] for j in range(DNA_length): if template[j]: DNA_a[j], DNA_b[j] = DNA_b[j], DNA_a[j] new_generation.append(DNA_a) new_generation.append(DNA_b) return new_generation def mutation(generation, p_m): new_generation = [] for DNA in generation: for j in range(len(DNA)): if random.random() < p_m: DNA[j] = (DNA[j] + 1) % 2 new_generation.append(DNA) return new_generation

crossover函数用于交叉个体的基因，其输入参数包括一个种群列表generation、基因长度DNA_length和交叉概率crossover_probability。该函数会生成一个模板template，用于决定哪些基因需要进行交叉。然后，该函数会按照...

class Node: def init(self, value): self.value = value self.prev = None self.next = None class DoublyLinkedList: def init(self): self.head = Node(None) def is_empty(self): return self.head.next == None def insert(self, value): new_node = Node(value) current_node = self.head while current_node.next != None: current_node = current_node.next current_node.next = new_node new_node.prev = current_node def get_length(self): count = 0 current_node = self.head while current_node.next != None: count += 1 current_node = current_node.next return count def insert_at(self, value, position): if position < 1 or position > self.get_length() + 1: print('Invalid position') return new_node = Node(value) current_node = self.head for i in range(position - 1): current_node = current_node.next new_node.prev = current_node new_node.next = current_node.next current_node.next.prev = new_node current_node.next = new_node def append(self, value): new_node = Node(value) current_node = self.head while current_node.next != None: current_node = current_node.next current_node.next = new_node new_node.prev = current_node def remove(self, value): current_node = self.head.next while current_node != None: if current_node.value == value: current_node.prev.next = current_node.next if current_node.next != None: current_node.next.prev = current_node.prev return current_node = current_node.next print('Value not found') def find(self, value): current_node = self.head.next while current_node != None: if current_node.value == value: return current_node current_node = current_node.next print('Value not found') def traverse_prev(self): current_node = self.head.next while current_node != None: print(current_node.value) current_node = current_node.prev def traverse_next(self): current_node = self.head.next while current_node != None: print(current_node.value) current_node = current_node.next #测试 dll = DoublyLinkedList() # (1)初始化一个双链表DLL。 print(dll.is_empty()) # (2)判断DLL是否为空。 dll.insert(14) #(3)将值为14、94、84、56、11的结点依次链入DLL中。 dll.insert(94) dll.insert(84) dll.insert(56) dll.insert(11) print(dll.get_length()) # (4)获取DLL的长度。 dll.insert_at(6, 3) # (5)将值为6的结点插至DLL中第3个位置。 dll.append(23) # (6)在DLL末端插入值为23的结点。 dll.remove(14) # (7)删除DLL中第1个位置的结点。 print(dll.find(94).value) # (8)查找DLL中值为94的结点。 dll.traverse_prev() # (9)按prev域依次遍历DLL中所有结点。 dll.traverse_next() # (10)按next域依次遍历DLL中所有结点。

这是一个双向链表的Python代码，包含了节点类和双向链表类。节点类包含了值、前驱和后继三个属性；双向链表类有头部节点，以及判断链表是否为空、插入元素、获取链表长度、在指定位置插入元素、在末尾添加元素...

time_new_list= data_integrity.iloc[-1, 0] minutes = int(time_new_list.minute) + int(time_new_list.hour) * 60 interval_length = ((minutes / 15) + 1) if interval_length == 96: dataset_pre = data_integrity.load.tail(int(interval_length)) else: dataset_pre = (data_integrity.load[:-int(interval_length)]).tail(96) date_history = pd.DataFrame(data_integrity.iloc[:, 0]) dataset_history = pd.DataFrame(data_integrity.iloc[:, 1]) # 数据划分完，数据格式转换 dataset_history = [each[0] for each in np.array(dataset_history.iloc[:, [0]]).tolist()] # 历史数据 data_history_conversion = [{i: dataset_history[i * 96:96 * (i + 1)]} for i in range(int(len(dataset_history) / 96))] # 历史数据转化 data_pre = [each for each in np.array(dataset_pre).tolist()] # 预测所需要的今日数据 data_pre_conversion = np.array([float(item) for item in data_pre[0:96]]) # 预测所需要的今日数据的格式转化代码优化

以下是代码优化后的版本： # 计算时间间隔 time_new_list = data_integrity.iloc[-1, 0] minutes = time_new_list.minute + time_new_list.hour * 60 interval_length = (minutes // 15) + 1 # 获取预测数据集...

def genetic_algorithm(F_time_list, int_result2, max_iteration=100): population_size = 10 chromosome_length = 10 population = [''.join([random.choice(['0', '1']) for _ in range(chromosome_length)]) for _ in range(population_size)] for i in range(max_iteration): fitness_scores = [fitness_score(chromosome, F_time_list, int_result2) for chromosome in population] if any([score == 0 for score in fitness_scores]): return population[fitness_scores.index(0)] parents = roulette_selection(population, fitness_scores) children = two_point_crossover(parents[0], parents[1]) mutated_children = [mutation(child) for child in children] population = population + mutated_children best_chromosome = max(population, key=lambda x: fitness_score(x, f_points, pf_lengths)) return best_chromosome print(genetic_algorithm(F_time_list, int_result2))

在你提供的代码中，存在一些错误： 1. for 循环缺少缩进，需要将 for 循环和后面的代码块缩进到同一层级。 2. f_points 和 pf_lengths 未定义，可能会导致 NameError 错误。你需要检查一下这两个变量是否...

for fish in fish_list: fish_pos=Vector2(fish[2],fish[3]) distanse2 = sp - fish_pos if distanse2.length() <= 50: img_fish = pygame.transform.rotozoom(img_fish, 0, 1.1) fish_list.remove(fish)改为比较大小消除小的的详细代码

如果要改为比较大小消除小的鱼，可以修改代码如下： python import pygame from pygame.math import Vector2 # 初始化pygame pygame.init() # 设置屏幕大小 screen_width = 640 screen_height = 480 screen = ...

import torch import torch.utils.data as Data import numpy as np class BatchDataset(Data.Dataset): def init(self, x, y): self.x = x self.y = y def len(self): return len(self.x) def getitem(self, index): return self.x[index], self.y[index] def split_data(stock, lookback, in_list, out_index): data_raw = stock.to_numpy() # convert to numpy array data = [] # create all possible sequences of length seq_len for index in range(len(data_raw) - lookback): data.append(data_raw[index: index + lookback]) data = np.array(data) test_set_size = int(np.round(0.2*data.shape[0])) train_set_size = data.shape[0] - (test_set_size) x_train = data[:train_set_size,:-1,in_list] y_train = data[:train_set_size,-1,out_index,None] x_test = data[train_set_size:,:-1,in_list] y_test = data[train_set_size:,-1,out_index,None] return [x_train, y_train, x_test, y_test] 请给我这段代码的伪代码

这这段这段代码这段代码主这段代码主要这段代码主要是这段代码主要是定义这段代码主要是定义了这段代码主要是定义了一个这段代码主要是定义了一个 Batch这段代码主要是定义了一个 BatchDataset这段代码主要是定义了...

校正以下代码的语法错误 def encode_edge(self, mode, node_history, node_history_st, edge_type, neighbors, neighbors_edge_value, first_history_indices, batch_size): max_hl = self.hyperparams['maximum_history_length'] max_neighbors = 0 for neighbor_states in neighbors: max_neighbors = max(max_neighbors, len(neighbor_states)) edge_states_list = list() # list of [#of neighbors, max_ht, state_dim] for i, neighbor_states in enumerate(neighbors): # Get neighbors for timestep in batch if len(neighbor_states) == 0: # There are no neighbors for edge type # TODO necessary? neighbor_state_length = int( np.sum([len(entity_dims) for entity_dims in self.state[edge_type[1]].values()]) ) edge_states_list.append(torch.zeros((1, max_hl + 1, neighbor_state_length), device=self.device)) else: edge_states_list.append(torch.stack(neighbor_states, dim=0).to(self.device)) # if self.hyperparams['edge_state_combine_method'] == 'sum': # Used in Structural-RNN to combine edges as well. op_applied_edge_states_list = list() for neighbors_state in edge_states_list: op_applied_edge_states_list.append(torch.sum(neighbors_state, dim=0))#torch.sum combined_neighbors = torch.stack(op_applied_edge_states_list, dim=0) # 获取combined_neighbors的第一个维度，代表邻接边的总数 combined_neighbors_0 = combined_neighbors.shape[0] # 创建全零矩阵s_next，形状为[batch_size, max_neighbors, combined_neighbors_0] s_next = torch.zeros((batch_size, max_neighbors, combined_neighbors_0), device=self.device) # 为s_next矩阵中每一行赋值为对应的combined_neighbors # for b in range(batch_size): # s_next[b, :len(neighbors[b]), :] = combined_neighbors[first_history_indices[b]] for i in range(batch_size): s_next[0, i, :] = batch_size[:] for i in range(max_neighbors): s_next[1, i, :] = max_neighbors[i, :] for i in range(combined_neighbors.shape[0]): s_next[2, i, :] = combined_neighbors

代码中缺少冒号，不是合法的 Python 代码。正确的代码应该是这样的： def encode_edge(self, mode, node_history, node_history_st, edge_type, neighbors, neighbors_edge_value, first_history_indices, ...

为每句代码做注释：for class_name in class_names: current_class_data_path = os.path.join(src_data_folder, class_name) current_all_data = os.listdir(current_class_data_path) current_data_length = len(current_all_data) current_data_index_list = list(range(current_data_length)) random.shuffle(current_data_index_list) train_folder = os.path.join(os.path.join(target_data_folder, 'train'), class_name) val_folder = os.path.join(os.path.join(target_data_folder, 'val'), class_name) test_folder = os.path.join(os.path.join(target_data_folder, 'test'), class_name) train_stop_flag = current_data_length * train_scale val_stop_flag = current_data_length * (train_scale + val_scale) current_idx = 0 train_num = 0 val_num = 0 test_num = 0 for i in current_data_index_list: src_img_path = os.path.join(current_class_data_path, current_all_data[i]) if current_idx <= train_stop_flag: copy2(src_img_path, train_folder) train_num = train_num + 1 elif (current_idx > train_stop_flag) and (current_idx <= val_stop_flag): copy2(src_img_path, val_folder) val_num = val_num + 1 else: copy2(src_img_path, test_folder) # print("{}复制到了{}".format(src_img_path, test_folder)) test_num = test_num + 1 current_idx = current_idx + 1

current_data_index_list = list(range(current_data_length)) # 随机打乱当前类别数据的索引列表 random.shuffle(current_data_index_list) # 拼接训练集、验证集、测试集的路径 train_folder = os.path....

for class_name in class_names: current_class_data_path = os.path.join(src_data_folder, class_name) current_all_data = os.listdir(current_class_data_path) current_data_length = len(current_all_data) current_data_index_list = list(range(current_data_length)) random.shuffle(current_data_index_list) train_folder = os.path.join(os.path.join(target_data_folder, 'train'), class_name) val_folder = os.path.join(os.path.join(target_data_folder, 'val'), class_name) test_folder = os.path.join(os.path.join(target_data_folder, 'test'), class_name) train_stop_flag = current_data_length * train_scale val_stop_flag = current_data_length * (train_scale + val_scale) current_idx = 0 train_num = 0 val_num = 0 test_num = 0 解释

然后，使用list函数和range函数结合，创建一个包含0到current_data_length-1的整数列表，并将结果保存在current_data_index_list变量中。这个列表用于后续对数据进行随机打乱。随后，通过os.path.join函数将目标...

程序运行提示IndexError: list index out of range，修改程序with open('image_skeleton_1.txt', 'r') as f: data = [] data_1 = [] for line in f: if line.strip(): # 去掉空行 line = line.strip().split()[0] # 只取第一列数据 if line not in data: data.append(line) for line_1 in f: if line_1.strip(): # 去掉空行 line_1 = line_1.strip().split()[1] # 只取第一列数据 if line_1 not in data_1: data_1.append(line_1) data = sorted([float(d) for d in data]) range_length = data[-1] - data[0] interval_length = range_length / 2 interval_1 = (data[0], data[0] + interval_length) interval_2 = (interval_1[1], data[-1]) data_1 = sorted([float(c) for c in data_1]) range_length_1 = data_1[-1] - data_1[0] interval_length_1 = range_length_1 / 3 interval_4 = (data_1[0], data_1[0] + interval_length_1) interval_5 = (interval_4[1], interval_4[1] + interval_length_1) interval_6 = (interval_5[1], data_1[-1])

问题出现在第二个for循环中，因为在第一个for循环中，f文件指针已经到达了文件末尾，所以在第二个for循环中没有数据可以读取，导致了IndexError: list index out of range错误。可以将第一个for循环读取的数据保存到...

# 输入10个字符到数组中 char_list = [] for i in range(10): char = input("请输入第{}个字符：".format(i+1)) char_list.append(char) # 将字符数组转换为字符串 string = "".join(char_list) # 统计大写字母、小写字母和其他字符的个数 upper_count = 0 lower_count = 0 other_count = 0 for char in string: if char.isupper(): upper_count += 1 elif char.islower(): lower_count += 1 else: other_count += 1 # 输出统计结果 print("大写字母个数：", upper_count) print("小写字母个数：", lower_count) print("其他字符个数：", other_count)java

抱歉，这段代码是 Python 代码，如果你想在 Java 中实现相同的功能，可以参考以下代码： java import java.util.Scanner; public class Main { public static void main(String[] args) { Scanner scanner = ...

current_class_data_path = os.path.join(src_data_folder, class_name) current_all_data = os.listdir(current_class_data_path) current_data_length = len(current_all_data) current_data_index_list = list(range(current_data_length)) random.shuffle(current_data_index_list) 解释代码

接着，使用 list 和 range 函数生成一个包含 0 到 current_data_length - 1 的整数列表，用于表示每个样本的索引。然后，使用 random.shuffle 方法随机打乱该列表，用于后续的数据划分。需要注意的是，该...

相关推荐

Python列表操作详解与示例

Java Stream API：10个顶级优化技巧，提升代码效率

将这段python代码转化为java :ef six2Json(result): res_json = dict() try: res_list = result.split("\r\n") for index in range(len(res_list) - 1): if index % 2 == 0: res_json[res_list[index]] = res_list[index + 1] except: pass return res_json

for fish in fish_list: fish_pos=Vector2(fish[2],fish[3]) distanse2 = sp - fish_pos if distanse2.length() <= 50: img_fish = pygame.transform.rotozoom(img_fish, 0, 1.1) fish_list.remove(fish)改为比较大小消除小的的详细代码

current_class_data_path = os.path.join(src_data_folder, class_name) current_all_data = os.listdir(current_class_data_path) current_data_length = len(current_all_data) current_data_index_list = list(range(current_data_length)) random.shuffle(current_data_index_list) 解释代码

最新推荐

Windows平台下的Fastboot工具使用指南

管理建模和仿真的文件

DLMS规约深度剖析：从基础到电力通信标准的全面掌握

修改代码，使其正确运行

Python机器学习基础入门与项目实践

"互动学习：行动中的多样性与论文攻读经历"

【Shell脚本进阶】：wc命令行数统计的高级用法及解决方案

python编写一个程序，使得根据输入的起点和终点坐标值计算出坐标方位角

Achilles-2 原始压缩包内容解密

关系数据表示学习