2.13生成可控的随机数据集合 生成九个分布的直方图

Posted 2020-09-02

import random
import matplotlib
import matplotlib.pyplot as plt

size=1000
bucket=100
plt.figure()
matplotlib.rcParams.update({‘font.size‘: 7})

plt.subplot(621)
plt.xlabel("random.random")
res = [random.random() for _a in xrange(1,size)]
plt.hist(res,bucket)

plt.subplot(622)
plt.xlabel("random.uniform")
a=1
b=size
res= [random.uniform(a,b) for _a in xrange(1,size)]
plt.hist(res,bucket)

plt.subplot(623)
plt.xlabel("random.triangular")
low=1
high = size
res = [random.triangular(low,high) for _A in xrange(1, size)]
plt.hist(res,bucket)

plt.subplot(624)
plt.xlabel("random.betavariate")
alpha =1
beta =10
res=[random.betavariate(alpha,beta) for _a in xrange(1,size)]
plt.hist(res,bucket)

plt.subplot(625)
plt.xlabel("random.expovariate")
lambd = 1.0/((size+1)/2)
res=[random.expovariate(lambd) for _a in xrange(1,size)]
plt.hist(res,bucket)

plt.subplot(626)
plt.xlabel("random.gammavariate")
alpha=1
beta=10
res=[random.gammavariate(alpha,beta) for _a in xrange(1,size)]
plt.hist(res,bucket)

plt.subplot(627)
plt.xlabel("random.lognorvariate")
mu=1
sigma=0.5
res=[random.lognormvariate(mu,sigma) for _a in xrange(1,size)]
plt.hist(res,bucket)

plt.subplot(628)
plt.xlabel("random.normalvariate")
mu=1
sigma=0.5
res=[random.normalvariate(mu,sigma) for _a in xrange(1,size)]
plt.hist(res,bucket)

plt.subplot(629)
plt.xlabel("random.paretovariate")
alpha=1

res=[random.paretovariate(alpha) for _a in xrange(1,size)]
plt.hist(res,bucket)

plt.tight_layout()
plt.show()