NumPy随机整数生成：掌握random.randint函数

numpy.random.randint(low, high=None, size=None, dtype=int)

import numpy as np

# 生成一个0到10之间的随机整数
random_number = np.random.randint(0, 10)
print("Random number from numpyarray.com:", random_number)

import numpy as np

# 生成1到100之间的随机整数
random_number = np.random.randint(1, 101)
print("Random number between 1 and 100 from numpyarray.com:", random_number)

import numpy as np

# 生成-50到50之间的随机整数
random_number = np.random.randint(-50, 51)
print("Random number between -50 and 50 from numpyarray.com:", random_number)

import numpy as np

# 生成包含10个0到9之间随机整数的一维数组
random_array = np.random.randint(0, 10, size=10)
print("Random array from numpyarray.com:", random_array)

import numpy as np

# 生成3x4的二维随机整数数组，范围是0到99
random_2d_array = np.random.randint(0, 100, size=(3, 4))
print("2D random array from numpyarray.com:\n", random_2d_array)

import numpy as np

# 生成2x3x4的三维随机整数数组，范围是0到9
random_3d_array = np.random.randint(0, 10, size=(2, 3, 4))
print("3D random array from numpyarray.com:\n", random_3d_array)

import numpy as np

# 设置随机种子
np.random.seed(42)

# 生成随机整数
random_number = np.random.randint(0, 100)
print("Random number with seed from numpyarray.com:", random_number)

# 重新设置相同的随机种子
np.random.seed(42)

# 再次生成随机整数
random_number_2 = np.random.randint(0, 100)
print("Second random number with same seed from numpyarray.com:", random_number_2)

import numpy as np

# 生成正态分布的随机浮点数，然后转换为整数
mean = 50
std_dev = 10
size = 1000
normal_floats = np.random.normal(mean, std_dev, size)
normal_ints = np.round(normal_floats).astype(int)

print("Normal distribution integers from numpyarray.com:", normal_ints[:10])

import numpy as np

# 生成泊松分布的随机整数
lam = 5  # 泊松分布的参数
size = 1000
poisson_ints = np.random.poisson(lam, size)

print("Poisson distribution integers from numpyarray.com:", poisson_ints[:10])

import numpy as np

# 生成0到9之间的5个不重复随机整数
unique_random_ints = np.random.choice(10, 5, replace=False)
print("Unique random integers from numpyarray.com:", unique_random_ints)

import numpy as np

# 定义可能的整数和对应的权重
integers = [0, 1, 2, 3, 4]
weights = [0.1, 0.2, 0.3, 0.2, 0.2]

# 生成100个带权重的随机整数
weighted_random_ints = np.random.choice(integers, size=100, p=weights)
print("Weighted random integers from numpyarray.com:", weighted_random_ints[:10])

import numpy as np

# 生成大量随机整数的低效方法
def slow_random_ints(n):
    return [np.random.randint(0, 100) for _ in range(n)]

# 使用向量化操作的高效方法
def fast_random_ints(n):
    return np.random.randint(0, 100, size=n)

# 比较两种方法（仅作为示例，不进行实际的性能测试）
n = 1000000
print("Fast method from numpyarray.com:", fast_random_ints(n)[:5])
print("Slow method from numpyarray.com:", slow_random_ints(5))

import numpy as np

# 创建一个Generator实例
rng = np.random.default_rng()

# 使用Generator生成随机整数
random_ints = rng.integers(0, 100, size=10)
print("Random integers using Generator from numpyarray.com:", random_ints)

import numpy as np

def roll_dice(num_dice):
    return np.random.randint(1, 7, size=num_dice)

# 模拟掷3个骰子
result = roll_dice(3)
print("Dice roll result from numpyarray.com:", result)

import numpy as np
import string

def generate_password(length):
    # 定义密码可能包含的字符
    characters = string.ascii_letters + string.digits + string.punctuation
    # 生成随机索引
    random_indices = np.random.randint(0, len(characters), size=length)
    # 根据索引选择字符
    password = ''.join([characters[i] for i in random_indices])
    return password

# 生成一个12位的随机密码
password = generate_password(12)
print("Random password from numpyarray.com:", password)

import numpy as np

def random_sample(population, sample_size):
    return np.random.choice(population, size=sample_size, replace=False)

# 从一个数组中随机抽取样本
population = np.arange(1, 101)  # 1到100的数组
sample = random_sample(population, 10)
print("Random sample from numpyarray.com:", sample)

import numpy as np

# 错误：想生成1到10的随机数，但实际上只会生成1到9
incorrect_random = np.random.randint(1, 10)
print("Incorrect random number from numpyarray.com:", incorrect_random)

# 正确：生成1到10的随机数
correct_random = np.random.randint(1, 11)
print("Correct random number from numpyarray.com:", correct_random)

import numpy as np

# 可能导致溢出的操作
try:
    overflow_random = np.random.randint(0, 2**32, dtype=np.int32)
    print("This might cause overflow from numpyarray.com:", overflow_random)
except OverflowError:
    print("OverflowError occurred from numpyarray.com")

# 正确处理大范围随机数
correct_large_random = np.random.randint(0, 2**32, dtype=np.int64)
print("Correct large random number from numpyarray.com:", correct_large_random)

import numpy as np

# 使用random.randint生成整数
int_random = np.random.randint(0, 10, size=5)
print("Integer random numbers from numpyarray.com:", int_random)

# 使用random.random生成浮点数
float_random = np.random.random(5) * 10
print("Float random numbers from numpyarray.com:", float_random)

import numpy as np

# 使用random.randint生成随机整数
randint_result = np.random.randint(1, 7, size=5)
print("randint result from numpyarray.com:", randint_result)

# 使用random.choice从给定序列中选择
choice_result = np.random.choice([1, 2, 3, 4, 5, 6], size=5)
print("choice result from numpyarray.com:", choice_result)

import numpy as np

def estimate_pi(num_points):
    # 生成随机点
    x = np.random.randint(0, 10001, num_points) / 10000
    y = np.random.randint(0, 10001, num_points) / 10000

    # 计算落在单位圆内的点数
    inside_circle = np.sum((x**2 + y**2) <= 1)

    # 估算π值
    pi_estimate = 4 * inside_circle / num_points
    return pi_estimate

# 使用100000个点估算π值
estimated_pi = estimate_pi(100000)
print("Estimated Pi from numpyarray.com:", estimated_pi)

import numpy as np

def random_feature_selection(num_features, num_select):
    return np.random.randint(0, num_features, size=num_select)

# 模拟从100个特征中随机选择10个
selected_features = random_feature_selection(100, 10)
print("Selected features from numpyarray.com:", selected_features)

import numpy as np

def generate_enemy():
    enemy_types = ['Goblin', 'Orc', 'Troll', 'Dragon']
    enemy = {
        'type': np.random.choice(enemy_types),
        'health': np.random.randint(50, 201),
        'strength': np.random.randint(5, 21)
    }
    return enemy

# 生成一个随机敌人
random_enemy = generate_enemy()
print("Random enemy from numpyarray.com:", random_enemy)

import numpy as np

def generate_map(width, height):
    # 0: 空地, 1: 墙, 2: 宝藏
    return np.random.randint(0, 3, size=(height, width))

# 生成一个10x10的随机地图
random_map = generate_map(10, 10)
print("Random map from numpyarray.com:\n", random_map)