Python – 统计中的 kappa4 分布

scipy.stats.kappa4()是一个 Kappa 4 连续随机变量，使用标准格式和一些形状参数定义以完成其规范。概率密度以标准形式定义，loc 和 scale 参数用于移动和/或缩放分布。

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 : kappa4 continuous random variable

代码 #1：创建 kappa4 连续随机变量

# importing library
  
from scipy.stats import kappa4  
    
numargs = kappa4.numargs 
a, b = 4.32, 3.18
rv = kappa4(a, b) 
    
print ("RV : \n", rv)  

输出 ：

RV : 
 scipy.stats._distn_infrastructure.rv_frozen object at 0x000002A9D50D62C8

代码 #2：Johnson SU 连续变量和概率分布

import numpy as np 
quantile = np.arange (0.01, 1, 0.1) 
  
# Random Variates 
R = kappa4.rvs(a, b, scale = 2, size = 10) 
print ("Random Variates : \n", R) 

输出 ：

Random Variates : 
 [0.62293659 0.62825781 0.62377628 0.62308697 0.62665555 0.62802109
 0.62872844 0.62728058 0.62679381 0.62297679]
 

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, 3)) 
print("Distribution : \n", distribution) 
     
plot = plt.plot(distribution, rv.pdf(distribution)) 

输出 ：

Distribution : 
 [0.         0.06122449 0.12244898 0.18367347 0.24489796 0.30612245
 0.36734694 0.42857143 0.48979592 0.55102041 0.6122449  0.67346939
 0.73469388 0.79591837 0.85714286 0.91836735 0.97959184 1.04081633
 1.10204082 1.16326531 1.2244898  1.28571429 1.34693878 1.40816327
 1.46938776 1.53061224 1.59183673 1.65306122 1.71428571 1.7755102
 1.83673469 1.89795918 1.95918367 2.02040816 2.08163265 2.14285714
 2.20408163 2.26530612 2.32653061 2.3877551  2.44897959 2.51020408
 2.57142857 2.63265306 2.69387755 2.75510204 2.81632653 2.87755102
 2.93877551 3.        ]
 

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 = kappa4 .pdf(x, 1, 3) 
y2 = kappa4 .pdf(x, 1, 4) 
plt.plot(x, y1, "*", x, y2, "r--") 

输出 ：