Python – 统计中的包裹柯西分布

scipy.stats.wrapcauchy()是一个包装的柯西连续随机变量。它作为rv_continuous 类的实例继承自泛型方法。它使用特定于此特定发行版的详细信息来完成方法。

参数：

q : lower and upper tail probability
x : quantiles
loc : [optional]location parameter. Default = 0
scale : [optional]scale parameter. Default = 1
size : [tuple of ints, optional] shape or random variates.
moments : [optional] composed of letters [‘mvsk’]; ‘m’ = mean, ‘v’ = variance, ‘s’ = Fisher’s skew and ‘k’ = Fisher’s kurtosis. (default = ‘mv’).

Results : wrapped Cauchy continuous random variable

编程需要懂一点英语

代码 #1：创建包装的 Cauchy 连续随机变量

# importing library
  
from scipy.stats import wrapcauchy 
    
numargs = wrapcauchy .numargs 
a, b = 0.2, 0.8
rv = wrapcauchy (a, b) 
    
print ("RV : \n", rv)

输出：

RV : 
 scipy.stats._distn_infrastructure.rv_frozen object at 0x000002A9DB1EFEC8

代码#2：包装的柯西连续变量和概率分布

import numpy as np 
quantile = np.arange (0.01, 1, 0.1) 
  
# Random Variates 
R = wrapcauchy .rvs(a, b, size = 10) 
print ("Random Variates : \n", R) 
  
# PDF 
x = np.linspace(wrapcauchy.ppf(0.01, a, b),
                wrapcauchy.ppf(0.99, a, b), 10)
R = wrapcauchy.pdf(x, 1, 3)
print ("\nProbability Distribution : \n", R)

输出：

Random Variates : 
 [4.86261249 3.08013229 2.12625546 2.11690773 4.07188434 3.3251514
 1.23202529 3.38334847 1.22842627 6.35459436]

Probability Distribution : 
 [nan nan nan nan nan nan nan nan nan nan]

代码#3：图形表示。

import numpy as np 
import matplotlib.pyplot as plt 
     
distribution = np.linspace(0, np.minimum(rv.dist.b, 2)) 
print("Distribution : \n", distribution)

输出：

Distribution : 
 [0.         0.04081633 0.08163265 0.12244898 0.16326531 0.20408163
 0.24489796 0.28571429 0.32653061 0.36734694 0.40816327 0.44897959
 0.48979592 0.53061224 0.57142857 0.6122449  0.65306122 0.69387755
 0.73469388 0.7755102  0.81632653 0.85714286 0.89795918 0.93877551
 0.97959184 1.02040816 1.06122449 1.10204082 1.14285714 1.18367347
 1.2244898  1.26530612 1.30612245 1.34693878 1.3877551  1.42857143
 1.46938776 1.51020408 1.55102041 1.59183673 1.63265306 1.67346939
 1.71428571 1.75510204 1.79591837 1.83673469 1.87755102 1.91836735
 1.95918367 2.        ]

代码#4：改变位置参数

import matplotlib.pyplot as plt 
import numpy as np 
  
x = np.linspace(0, 5, 100) 
     
# Varying positional arguments 
y1 = wrapcauchy.pdf(x, a, b) 
y2 = wrapcauchy.pdf(x, a, b) 
plt.plot(x, y1, "*", x, y2, "r--")

输出：