Lexan 2020-04-15
random
内建模块,伪随机数生成器使用Mersenne Twister的伪随机数生成器PRNG进行生成,它以一个确定的数字作为属于,并为其生成一个随机数;为了安全起见,不要用PRNG生成随机数,要用secrets模块的真随机数TRNG生成;
>>> import random ## 1、当不指定种子seed时,PRNG每次生成的数不一样 >>> print(‘Random Number 1=>‘,random.random()) Random Number 1=> 0.21008902332926982 >>> print(‘Random Number 2=>‘,random.random()) Random Number 2=> 0.434434837731393 ## 2、当指定种子seed时,PRNG每次生成的数是一样的,所以称为伪随机数 >>> random.seed(42) >>> print(‘Random Number 1=>‘,random.random()) Random Number 1=> 0.6394267984578837 >>> random.seed(42) >>> print(‘Random Number 2=>‘,random.random()) Random Number 2=> 0.6394267984578837
random.random()*3 + 2
, uniform(2,5), randint(2,5)## 1、random.random()*3 + 2 >>> print(‘Random Number in range(2,8)=>‘, random.random()*6+2) Random Number in range(2,8)=> 2.1500645313360014 ## 2、uniform():获取开始值和结束值作为参数,返回一个浮点型的随机数 >>> print(‘Random Number in range(2,8)=>‘, random.uniform(2,8)) Random Number in range(2,8)=> 3.6501759102147155 ## 3、randint():和uniform相似,不同的是返回值为一个整数 >>> print(‘Random Number in range(2,8)=>‘, random.randint(2,8)) Random Number in range(2,8)=> 3
## 1、choice会从这个列表中随机选择一个值 >>> a=[5,9,20,10,2,8] >>> print(‘Randomly picked number=>‘,random.choice(a)) Randomly picked number=> 9 >>> print(‘Randomly picked number=>‘,random.choice(a)) Randomly picked number=> 8 >>> print(‘Randomly picked number=>‘,random.choice(a)) Randomly picked number=> 5 ## 2、choices会从这个列表中随机选择多个值(随机数的数量可以超过列表程度) >>> print(‘Randomly picked number=>‘,random.choices(a,k=3)) Randomly picked number=> [5, 20, 5] >>> print(‘Randomly picked number=>‘,random.choices(a,k=3)) Randomly picked number=> [9, 10, 5] >>> print(‘Randomly picked number=>‘,random.choices(a,k=3)) Randomly picked number=> [9, 10, 10] ## 3、choices利用weights将数组作为权重传递,增加每个值被选取的可能性 >>> print(‘Randomly picked number=>‘,random.choices(a,weights=[1,1,1,3,1,1],k=3)) Randomly picked number=> [5, 5, 2] >>> print(‘Randomly picked number=>‘,random.choices(a,weights=[1,1,1,3,1,1],k=3)) Randomly picked number=> [10, 2, 10] >>> print(‘Randomly picked number=>‘,random.choices(a,weights=[1,1,1,3,1,1],k=3)) Randomly picked number=> [10, 8, 10]
>>> print(‘Original list=>‘,a) Original list=> [5, 9, 20, 10, 2, 8] >>> random.shuffle(a) >>> print(‘Shuffled list=>‘,a) Shuffled list=> [10, 5, 8, 9, 2, 20]
(1)高斯分布gauss()
>>> import random >>> import matplotlib.pyplot as plt >>> temp = [] >>> for i in range(1000): ... temp.append(random.gauss(0,1)) ... >>> plt.hist(temp, bins=30) >>> plt.show()
(2)变数分布expovariate():以lambda的值作为参数,lambda为正,则返回从0到正无穷的值;如果lambda为负,则返回从负无穷到0的值
>>> print(‘Random number from exponential distribution=>‘,random.expovariate(10)) Random number from exponential distribution=> 0.012164560954097013 >>> print(‘Random number from exponential distribution=>‘,random.expovariate(-1)) Random number from exponential distribution=> -0.6461397037921695
(3)伯努利分布
(4)均匀分布
(5)二项分布
(6)正太分布
(7)泊松分布
参考:https://www.analyticsvidhya.com/blog/2020/04/how-to-generate-random-numbers-in-python/?utm_source=feedburner&utm_medium=email&utm_campaign=Feed%3A+AnalyticsVidhya+(Analytics+Vidhya)
其他六种不同的概率分布:https://www.analyticsvidhya.com/blog/2017/09/6-probability-distributions-data-science/?utm_source=blog&utm_medium=how-to-generate-random-numbers-in-python