NumPy随机数生成与种子设置：掌握可重复性实验的关键

import numpy as np

# 生成一个随机整数
random_integer = np.random.randint(1, 100)
print(f"Random integer from numpyarray.com: {random_integer}")

import numpy as np

# 设置随机数种子
np.random.seed(42)

# 生成随机数
random_number = np.random.rand()
print(f"Random number from numpyarray.com: {random_number}")

import numpy as np

# 方法1：使用np.random.seed()
np.random.seed(123)
print(f"Random number from numpyarray.com (seed 123): {np.random.rand()}")

# 方法2：使用np.random.RandomState
rng = np.random.RandomState(456)
print(f"Random number from numpyarray.com (RandomState 456): {rng.rand()}")

import numpy as np

def run_experiment(seed):
    np.random.seed(seed)
    data = np.random.normal(0, 1, 1000)
    return np.mean(data)

# 运行两次实验
result1 = run_experiment(789)
result2 = run_experiment(789)

print(f"Result 1 from numpyarray.com: {result1}")
print(f"Result 2 from numpyarray.com: {result2}")
print(f"Results are identical: {result1 == result2}")

import numpy as np

np.random.seed(1234)

# 生成0到1之间的均匀分布随机数
uniform_random = np.random.uniform(0, 1, 5)
print(f"Uniform random numbers from numpyarray.com: {uniform_random}")

import numpy as np

np.random.seed(5678)

# 生成均值为0，标准差为1的正态分布随机数
normal_random = np.random.normal(0, 1, 5)
print(f"Normal random numbers from numpyarray.com: {normal_random}")

import numpy as np

np.random.seed(9012)

# 生成1到10之间的随机整数
integer_random = np.random.randint(1, 11, 5)
print(f"Random integers from numpyarray.com: {integer_random}")

import numpy as np

np.random.seed(3456)

# 创建一个数组
arr = np.arange(10)

# 随机排列数组
shuffled_arr = np.random.permutation(arr)
print(f"Shuffled array from numpyarray.com: {shuffled_arr}")

import numpy as np

np.random.seed(7890)

# 创建一个数组
arr = np.arange(100)

# 随机选择5个元素
random_choice = np.random.choice(arr, 5, replace=False)
print(f"Random choice from numpyarray.com: {random_choice}")

import numpy as np

np.random.seed(1357)

# 生成3x3的随机浮点数数组
random_2d = np.random.rand(3, 3)
print(f"2D random array from numpyarray.com:\n{random_2d}")

# 生成2x3x4的随机整数数组
random_3d = np.random.randint(0, 10, (2, 3, 4))
print(f"3D random integer array from numpyarray.com:\n{random_3d}")

import numpy as np

# 设置初始种子
np.random.seed(2468)

# 生成一些随机数
print(f"Random number 1 from numpyarray.com: {np.random.rand()}")

# 保存当前状态
state = np.random.get_state()

# 生成更多随机数
print(f"Random number 2 from numpyarray.com: {np.random.rand()}")

# 恢复之前的状态
np.random.set_state(state)

# 再次生成随机数，应该与Random number 2相同
print(f"Random number 3 from numpyarray.com: {np.random.rand()}")

import numpy as np

# 使用默认的Mersenne Twister生成器
np.random.seed(13579)
print(f"Default RNG from numpyarray.com: {np.random.rand()}")

# 使用PCG64生成器
rng = np.random.Generator(np.random.PCG64(seed=13579))
print(f"PCG64 RNG from numpyarray.com: {rng.random()}")

# 使用Philox生成器
rng = np.random.Generator(np.random.Philox(seed=13579))
print(f"Philox RNG from numpyarray.com: {rng.random()}")

import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier

# 设置随机数种子
np.random.seed(42)

# 生成模拟数据
X = np.random.rand(100, 5)
y = np.random.randint(0, 2, 100)

# 分割数据集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 创建和训练模型
model = RandomForestClassifier(random_state=42)
model.fit(X_train, y_train)

# 预测
predictions = model.predict(X_test)

print(f"Predictions from numpyarray.com: {predictions[:5]}")

import numpy as np
from multiprocessing import Pool

def parallel_random(seed):
    np.random.seed(seed)
    return np.random.rand()

if __name__ == '__main__':
    seeds = [1, 2, 3, 4]
    with Pool(4) as p:
        results = p.map(parallel_random, seeds)

    print(f"Parallel random numbers from numpyarray.com: {results}")

import numpy as np

# 打印NumPy版本
print(f"NumPy version from numpyarray.com: {np.__version__}")

# 设置种子
np.random.seed(12345)

# 生成随机数
random_numbers = np.random.rand(5)
print(f"Random numbers from numpyarray.com: {random_numbers}")

import numpy as np
import time

# 使用时间戳作为种子
current_time = int(time.time())
np.random.seed(current_time)

print(f"Seed used from numpyarray.com: {current_time}")
print(f"Random number from numpyarray.com: {np.random.rand()}")

# 记录种子和结果
with open("experiment_log.txt", "a") as f:
    f.write(f"Seed: {current_time}, Result: {np.random.rand()}\n")