Python – 统计中的逆威布尔分布

scipy.stats.invweibull()是一个倒置的 weibull 连续随机变量，它使用标准格式和一些形状参数定义以完成其规范

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 : Inverse weibull continuous random variable

编程需要懂一点英语

代码#1：创建倒置威布尔连续随机变量

# importing library
from scipy.stats import invweibull  
    
numargs = invweibull.numargs 
[a] = [0.6, ] * numargs 
rv = invweibull(a) 
    
print ("RV : \n", rv)

输出：

RV : 
 scipy.stats._distn_infrastructure.rv_frozen object at 0x000002A9D4EAE9C8

代码#2：倒置威布尔连续变量和概率分布

import numpy as np 
quantile = np.arange (0.01, 1, 0.1) 
  
# Random Variates 
R = invweibull.rvs(a, scale = 2, size = 10) 
print ("Random Variates : \n", R) 
  
# PDF 
R = invweibull.pdf(a, quantile, loc = 0, scale = 1) 
print ("\nProbability Distribution : \n", R)

输出：

Random Variates : 
 [ 2.46502056 32.97160826  8.65843435  1.21357636  0.22162243  1.05724138
  7.5574935   0.0624836   0.83384033 17.29417907]

Probability Distribution : 
 [0.00613124 0.06733615 0.12799203 0.18757349 0.24553408 0.30131353
 0.35434638 0.40407156 0.44994318 0.49144206]

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

输出：