下面是一个示例代码,演示了如何在给定的区间内生成正态分布随机数: importrandomdefgenerate_normal_distribution(start,end,mu,sigma,num_samples):samples=[]whilelen(samples)<num_samples:x=random.gauss(mu,sigma)ifstart<=x<=end:samples.append(x)returnsamples start=0end=10mu=5sigma=2num_samples=1000sa...
# It will generate same random number random.seed(0) print "Random number with seed 0 : ", random.random() import numpy random.seed( 10 ) numpy.random.seed(10) print "Random number with seed 10 : ", random.random() print "Numpy.Random number with seed 10 : ", numpy.random.random...
results = [random.randint(1, 6) for _ in range(n)] return results print(roll_dice(10)) # 模拟投掷 10 次骰子 示例2:生成随机密码 随机生成一个包含大小写字母和数字的密码。 import random import string def generate_password(length): chars = string.ascii_letters + string.digits password = '...
## --- uniform distribution --- def uniform(self, a, b): "Get a random number in the range [a, b) or [a, b] depending on rounding." return a + (b-a) * self.random() ## --- triangular --- def triangular(self, low=0.0, high=1.0, mode=None): """Triangular distribution...
随机变量(Random Variable) 密度函数(Density Functions) 伯努利分布(Bernoulli Distribution) 二项式分布(Binomial Distribution) 均匀分布(Uniform Distribution) 泊松分布(Poisson Distribution) 正态分布(Normal Distribution) 长尾分布(Long-Tailed Distribution)
我们先用random生成随机数 import random # 生成伪随机数 def generate_pseudo_random(seed): random.seed(seed) # 设置随机数种子 return [random.randint(1, 100) for _ in range(5)] # 生成5个随机整数 # 初始种子 initial_seed = 42 print(f"初始种子:{initial_seed}") # 第一次生成伪随机数 rand...
% 'randn' is initially used to generate m sets of n % random variables with independent multivariate % normal distribution, with mean 0 and variance 1. % Then the incomplete gamma function, 'gammainc', % is used to map these points radially to fit in the ...
generate random permutation distributions on the real line: --- uniform triangular normal (Gaussian) lognormal negative exponential gamma beta pareto Weibull distributions on the circle (angles 0 to 2pi) --- circular uniform von Mises ``` 1.3...
随机变量(Random Variable) 密度函数(Density Functions) 伯努利分布(Bernoulli Distribution) 二项式分布(Binomial Distribution) 均匀分布(Uniform Distribution) 泊松分布(Poisson Distribution) 正态分布(Normal Distribution) 长尾分布(Long-Tailed Distribution)
b=[]# Generating 1000 points from normal distribution.foriinrange(1000):a=12.+np.random.standard_normal(100)b.append(np.product(a))# Making all negative values into positivesb=np.array(b)/np.min(b)count,bins,ignored=plt.hist(b,100,density=True,color='green')sigma=np.std(np.log(...