random.choice(s)

优化：def generate_data(add_user): # 学/工号 id_num = random.randint(100000, 999999) # 姓名 first_names = ['张', '王', '李', '刘', '赵', '陈', '周', '钱', "孙", "吴", "曾", "冯", "陶", "将", "黄", "胡", "朱", "杨", "江", "何"] s_names = ['云', '风', '山', '河', '林', '羽', '行', "雪", '月', '雨', '冰', '雷', '鞥', '时', '文', '代', '格', '台', "发", '方', '欧', '白'] l_names = ['雷', '鞥', '时', '文', '代', '格', '台', "发", '方', '欧', '白'] name = random.choice(first_names) + random.choice(s_names) + random.choice(l_names) # 性别 gender = random.choice(['男', '女']) # 身份 identity = random.choice(["学生"]) # 部门 department = random.choice( ["20230608094700005793941100036910"]) # 手机号 phone_num = '1' + str(random.randint(3, 9)) + ''.join(str(random.randint(0, 9)) for _ in range(9)) id_types = ['身份证', '护照', '驾驶证', '学生证', '工作证'] if add_user: id_type = random.choice(id_types) if id_type == '身份证': id_num = ''.join(str(random.randint(0, 9)) for _ in range(18)) elif id_type == '护照': id_num = ''.join(str(random.randint(0, 9)) for _ in range(9)) elif id_type == '驾驶证': id_num = ''.join(str(random.randint(0, 9)) for _ in range(12)) elif id_type == '学生证': id_num = ''.join(str(random.randint(0, 9)) for _ in range(10)) else: id_num = ''.join(str(random.randint(0, 9)) for _ in range(8)) return [id_num, name, gender, phone_num, identity, department, phone_num, id_type, id_num] else: return [name, gender, identity, department, phone_num]

name = random.choice(first_names) + random.choice(s_names) + random.choice(l_names) # 性别 gender = random.choice(['男', '女']) # 身份 identity = random.choice(["学生"]) # 部门 department = ...

解释每一句import pymysql class MyMysql: def init(self): self.connect = pymysql.connect( host=Mysql服务IP地址, port=Mysql数据库端口, user=数据库用户名, password=数据库密码, database=数据库名, charset='utf8' ) self.cursor = self.connect.cursor()import random from tools.config import MyMysql class UaPool: def init(self): database = MyMysql() db = database.connect cur = database.cursor sql = 'select useragent from ua_pool where types = %s and popularity != %s' cur.execute(sql, ('Windows', 'Uncommon')) data_d = cur.fetchall() desktop_list = [] for data_d_per in data_d: desktop_list.append(data_d_per[0]) self.desktop = desktop_list cur.execute(sql, ('Android', 'Uncommon')) data_m = cur.fetchall() mobile_list = [] for data_m_per in data_m: mobile_list.append(data_m_per[0]) self.mobile = mobile_list cur.close() db.close() def choose_ua(self, platform='desktop'): if platform == 'desktop': return random.choice(self.desktop) elif platform == 'mobile': return random.choice(self.mobile) else: return random.choice(self.desktop) if name == 'main': up = UaPool() print(up.choose_ua())

29. return random.choice(self.desktop)：从 Windows 桌面平台上不常见的用户代理池中随机选择一个用户代理并返回。 30. elif platform == 'mobile':：如果指定平台是移动平台。 31. return random.choice...

def sample_data(filepath,idx,path): f = open(filepath, 'rb') bi = pickle.load(f) data = np.arange(0, idx) data = data.tolist() samples = [] bs = [] a = path r = math.ceil(len(data)/a) for i in range(a): if len(data) > r: sample = np.random.choice(data, r, replace=False) else: sample = np.random.choice(data, len(data), replace=False) b = [bi[j] for j in list(sample)] bs.append(b) samples.append(sample) for s in sample: data.remove(s) samples = [np.array(s) for s in samples] samples = np.concatenate(samples, axis=0) bs = [np.array(e) for e in bs] bs = np.concatenate(bs, axis=0) return samples, bs请帮我输出每一次的b值并且将导入dataload里面可以用于深度学习

sample = np.random.choice(data, len(data), replace=False) b = [bi[j] for j in list(sample)] bs.append(b) samples.append(sample) for s in sample: data.remove(s) print("b值为：", b) # 输出每一次...

import pickle import math import numpy as np from torch.utils.data import DataLoader, TensorDataset def sample_data(filepath, idx, path): f = open(filepath, 'rb') bi = pickle.load(f) data = np.arange(0, idx) data = data.tolist() samples = [] bs = [] a = path r = math.ceil(len(data) / a) for i in range(a): if len(data) > r: sample = np.random.choice(data, r, replace=False) else: sample = np.random.choice(data, len(data), replace=False) b = [bi[j] for j in list(sample)] bs.append(b) samples.append(sample) for s in sample: data.remove(s) samples = [np.array(s) for s in samples] samples = np.concatenate(samples, axis=0) bs = [np.array(e) for e in bs] bs = np.concatenate(bs, axis=0) return samples, bs如何让每一次for循环中的b值都能单独进行一次深度学习的dataloader，最后遍历完for循环中所有的b值

sample = np.random.choice(data, len(data), replace=False) b = [bi[j] for j in list(sample)] bs.append(b) samples.append(sample) for s in sample: data.remove(s) # 将b值转换为TensorDataset...

# 设置变异率 # 进行迭代 for i in range(generations): # 选择 fits = [fitness_function(individual) for individual in population] indices = np.argsort(fits) population = [population[i] for i in indices[:population_size//2]] # 交叉 for i in range(population_size//2): individual1, individual2 = random.choice(population), random.choice(population) child1, child2 = crossover(individual1, individual2) population += [child1, child2] # 变异 for i in range(population_size): population[i] = mutate(population[i],mutation_rate) # 返回最优解 best_solution = max(population, key=fitness_function) return best_solution # 运行优化程序 returns = mu cov_matrix = S weights = optimize_portfolio_with_genetic_algorithm() print(weights)算出来的权重和为多少

遗传算法优化投资组合权重的过程中，权重并没有被限制为必须相加为1，因此它们的和可能会超过1或少于1。如果我们想要将它们限制为相加为1，可以使用下面的代码： weights /= np.sum(weights) ...

请帮我将这段代码改为设置8个固定的NPC代码，并加入设置每个NPC的速度和加速度以及行驶路线的代码# Spawn vehicles # -------------- batch = [] for n, transform in enumerate(spawn_points): print('n',n) print('transform',transform) if n >= args.number_of_vehicles: break blueprint = random.choice(blueprints) print('blueprints的长度', len(blueprints)) print('blueprints',blueprints) print('blueprint', blueprint) if blueprint.has_attribute('color'): color = random.choice(blueprint.get_attribute('color').recommended_values) blueprint.set_attribute('color', color) if blueprint.has_attribute('driver_id'): driver_id = random.choice(blueprint.get_attribute('driver_id').recommended_values) blueprint.set_attribute('driver_id', driver_id) blueprint.set_attribute('role_name', 'autopilot') # prepare the light state of the cars to spawn light_state = vls.NONE if args.car_lights_on: light_state = vls.Position | vls.LowBeam | vls.LowBeam # spawn the cars and set their autopilot and light state all together batch.append(SpawnActor(blueprint, transform) .then(SetAutopilot(FutureActor, True, traffic_manager.get_port())) .then(SetVehicleLightState(FutureActor, light_state))) for response in client.apply_batch_sync(batch, synchronous_master): if response.error: logging.error(response.error) else: vehicles_list.append(response.actor_id)

color = random.choice(blueprint.get_attribute('color').recommended_values) blueprint.set_attribute('color', color) driver_id = random.choice(blueprint.get_attribute('driver_id').recommended_values)...

import numpy as np import pickle f = open(r'C:\Users\sdnugeo\Desktop\target2.pkl','rb') bi = pickle.load(f) # bi = np.array(bi) data = np.arange(0, 156) print(data) data = data.tolist() # print(data) # 每次抽取1000个数值，共抽取10次 samples = [] c = [] for i in range(11): if len(data) > 15: sample = np.random.choice(data, 15, replace=False) else: sample = np.random.choice(data, 6, replace=False) # continue # print(type(sample)) # sample2 = np.array(sample) b = [bi[j] for j in list(sample)] # d = np.array(b) print(type(b)) c.append(b) # c = bi[0] print(sample) print(b) samples.append(sample) # sample = [s for s in sample if s in data] for s in sample: data.remove(s) samples = np.array(samples).reshape(-1) print(samples) print(data)，报错setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (11,) + inhomogeneous part.

samples = [np.array(s) for s in samples] samples = np.concatenate(samples, axis=0) 这样就可以将 samples 转换成一维的 NumPy 数组。另外，你在循环中的最后一次循环中没有把 sample 中的元素移除 ...

data = np.r_[1:1000] data = list(data) print(data) # 每次抽取1000个数值，共抽取10次 samples = [] for i in range(2): sample = np.random.choice(data, 10) samples.append(sample) print(sample) data.remove(sample) print(data)请帮我修改这个代码

sample = np.random.choice(data, 10) samples.append(sample) for s in sample: data.remove(s) print(samples) print(data) 这样修改后的代码可以正确地抽取10次样本，并将已选中的样本从数据集中移除。

def update(self, s0, a0, r, s1): self.q_learning(s0, a0, r, s1) self.model[(s0, a0)] = r, s1 # 将数据添加到模型中 for _ in range(self.n_planning): # Q-planning循环 # 随机选择曾经遇到过的状态动作对 (s, a), (r, s_) = random.choice(list(self.model.items())) self.q_learning(s, a, r, s_) 中for _ in range(self.n_planning)详细解释

1. 从self.model中随机选择一个曾经遇到过的状态动作对，即(s, a)，其中r是奖励值，s_是下一个状态。 2. 调用self.q_learning(s, a, r, s_)函数，对选择的状态动作对进行一次Q-learning更新。这一步的目的是根据当前...

import reimport randomdef random_combination(s): pattern = re.compile(r'\((.*?)\)') while True: match = pattern.search(s) if not match: break sub_str = match.group(1) choices = sub_str.split('|') choices = [c for c in choices if c] # 去除空字符串 if not choices: return '' s = s[:match.start()] + random.choice(choices) + s[match.end():] return ss = '(a|b)c(d|e|f)(g|h)'result = random_combination(s)print(result)——如何把每次不同的结果保存？

s = s[:match.start()] + random.choice(choices) + s[match.end():] results.append(s) s = '(a|b)c(d|e|f)(g|h)' results = [] random_combination(s, results) print(results) # 输出：['acdg'] 在上述代码...

import numpy as np import pickle import math f = open(r'C:\Users\sdnugeo\Desktop\target2.pkl','rb') bi = pickle.load(f) # bi = np.array(bi) data = np.arange(0, 156) print(data) data = data.tolist() # print(data) # 每次抽取1000个数值，共抽取10次 samples = [] c = [] a = 11 r = math.ceil(len(data)/a) print(r) for i in range(a): if len(data) > r : sample = np.random.choice(data, r, replace=False) else: sample = np.random.choice(data, len(data), replace=False) # for s in sample: # data.remove(s) # continue # print(type(sample)) # sample2 = np.array(sample) b = [bi[j] for j in list(sample)] # d = np.array(b) print(type(b)) c.append(b) # c = np.concatenate(c, axis=0) # c = bi[0] print(sample) # print(b) samples.append(sample) for s in sample: data.remove(s) # sample = [s for s in sample if s in data] samples = [np.array(s) for s in samples] samples = np.concatenate(samples, axis=0) c = [np.array(e) for e in c] c = np.concatenate(c, axis=0) # samples = np.array(samples).reshape(-1) print(samples) print(c.shape) 请帮我把这段代码修改成可调用的函数

sample = np.random.choice(data, len(data), replace=False) b = [bi[j] for j in list(sample)] c.append(b) samples.append(sample) for s in sample: data.remove(s) samples = [np.array(s) for s in ...

You could also create code to simulate the Monty Hall problem. Create a function named random_door, which uses numpy.random.choice to sample 1 door randomly from a list of integer door indices (1-3 in this case). Use this function to randomly select the door the car is behind and the contestant’s initial choice of doors. Create a function monty_choice, which chooses the door Monty opens, conditional on the contestant’s choice of doors and the door with the car. For the case where the contestant has selected the door with the car, select the door to open by simulating the flip of a fair coin using the np.random.binomial function with n = 1. Create a function win_car, which determines if the contestant wins the car, conditional on the strategy selected, {switch, noswitch}, the door the contestant selected, the door with the car, and the door Monty opened. Create a function named simulation that allows you to run the simulation n = 1000 times, with a switch or noswitch strategy. Execute your simulation for each possible strategy. For the two strategies, plot side by side bar charts showing the numbers of successes and failures for each strategy. Describe the strategy a contestant should adopt for this game. How much will the chosen strategy change the probability of winning a car? Is this result consistent with the conditional probability of this problem?

This code defines three functions: random_door to randomly select a door for the car or the contestant, monty_choice to choose the door Monty opens based on the contestant's choice and the ...

解释： def q_learning(self, s0, a0, r, s1): td_error = r + self.gamma * self.Q_table[s1].max( ) - self.Q_table[s0, a0] self.Q_table[s0, a0] += self.alpha * td_error def update(self, s0, a0, r, s1): self.q_learning(s0, a0, r, s1) self.model[(s0, a0)] = r, s1 # 将数据添加到模型中 for _ in range(self.n_planning): # Q-planning循环 # 随机选择曾经遇到过的状态动作对 (s, a), (r, s_) = random.choice(list(self.model.items())) self.q_learning(s, a, r, s_)

- 从self.model中随机选择一个曾经遇到过的状态动作对，即(s, a)和对应的(r, s_)。 - 调用q_learning函数对选择的状态动作对进行一次Q-learning更新，以进一步改进Q值表。 - 这个函数的目的是将当前的状态动作对...

基于java的贝儿米幼儿教育管理系统答辩PPT.pptx

课设毕设基于SpringBoot+Vue的养老院管理系统的设计与实现源码可运行.zip

本压缩包资源说明，你现在往下拉可以看到压缩包内容目录我是批量上传的基于SpringBoot+Vue的项目，所以描述都一样；有源码有数据库脚本，系统都是测试过可运行的，看文件名即可区分项目~ ｜Java｜SpringBoot｜Vue｜前后端分离｜开发语言：Java 框架：SpringBoot，Vue JDK版本：JDK1.8 数据库：MySQL 5.7+（推荐5.7，8.0也可以）数据库工具：Navicat 开发软件： idea/eclipse（推荐idea） Maven包：Maven3.3.9+ 系统环境：Windows/Mac

相关推荐

python np.random.choice方法

「Python系列」Python random模块、hashlib模块.md

Python使用random模块生成随机数操作实例详解

data = np.r_[1:1000] data = list(data) print(data) # 每次抽取1000个数值，共抽取10次 samples = [] for i in range(2): sample = np.random.choice(data, 10) samples.append(sample) print(sample) data.remove(sample) print(data)请帮我修改这个代码

基于java的贝儿米幼儿教育管理系统答辩PPT.pptx

课设毕设基于SpringBoot+Vue的养老院管理系统的设计与实现源码可运行.zip

最新推荐

基于java的贝儿米幼儿教育管理系统答辩PPT.pptx

课设毕设基于SpringBoot+Vue的养老院管理系统的设计与实现源码可运行.zip

基于java的消防物资存储系统答辩PPT.pptx

【java毕业设计】饮食营养管理信息系统源码（springboot+vue+mysql+说明文档）.zip

Aspose资源包：转PDF无水印学习工具

管理建模和仿真的文件

【R语言高性能计算秘诀】：代码优化，提升分析效率的专家级方法

在构建视频会议系统时，如何通过H.323协议实现音视频流的高效传输，并确保通信的稳定性？

Go语言控制台输入输出操作教程

"互动学习：行动中的多样性与论文攻读经历"