Python实现按比例产生随机数的两种方法

import random

def random_01():
    return 0 if random.random() < 0.7 else 1

# 验证概率分布
from collections import Counter
results = [random_01() for _ in range(10000)]
print(Counter(results))  # 输出示例：Counter({0: 7023, 1: 2977})

import numpy as np

def random_01_np():
    return np.random.choice([0, 1], p=[0.7, 0.3])

# 验证概率分布
results = [random_01_np() for _ in range(10000)]
print(Counter(results))  # 输出示例：Counter({0: 7012, 1: 2988})

np.random.choice([0, 1], size=1000, p=[0.7, 0.3])

def p_random(arr1,arr2):
    assert len(arr1) == len(arr2), "Length does not match."
    assert sum(arr2) == 1 , "Total rate is not 1."

    sup_list = [len(str(i).split(".")[-1]) for i in arr2]
    top = 10 ** max(sup_list)
    new_rate = [int(i*top) for i in arr2]
    rate_arr = []
    for i in range(1,len(new_rate)+1):
        rate_arr.append(sum(new_rate[:i]))
    rand = random.randint(1,top)
    data = None
    for i in range(len(rate_arr)):
        if rand <= rate_arr[i]:
            data = arr1[i]
            break
    return data

plist = []
for i in range(100000):
    plist.append( p_random([1,2,3],[0.209,0.291,0.5]))
print(Counter(plist))
#输出结果：
Counter({3: 50100, 2: 29132, 1: 20768})