scipy stats.chi() | Python

scipy.stats.chi()是一个 chi 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。

Parameters :
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 : chi continuous random variable

特别案例 ：

• chi(1, loc, scale) =半正态
• chi(2, 0, scale) =瑞利
• chi(3, 0, scale) :麦克斯韦

代码 #1：创建 chi 连续随机变量

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

输出 ：

RV : 
 

代码#2：chi 随机变量和概率分布。

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

输出 ：

Random Variates : 
 [2.40483665 1.68478304 0.01664071 2.48977805 3.66286843 1.68463842
 0.14434643 0.67812242 0.46190886 1.99973997]

Probability Distribution : 
 [0.01384193 0.14349716 0.25719966 0.35519439 0.43801475 0.50641521
 0.56131243 0.60373433 0.63477687 0.65556791]
 

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, 5))
print("Distribution : \n", distribution)
  
plot = plt.plot(distribution, rv.pdf(distribution))

输出 ：

Distribution : 
Distribution : 
 [0.         0.10204082 0.20408163 0.30612245 0.40816327 0.51020408
 0.6122449  0.71428571 0.81632653 0.91836735 1.02040816 1.12244898
 1.2244898  1.32653061 1.42857143 1.53061224 1.63265306 1.73469388
 1.83673469 1.93877551 2.04081633 2.14285714 2.24489796 2.34693878
 2.44897959 2.55102041 2.65306122 2.75510204 2.85714286 2.95918367
 3.06122449 3.16326531 3.26530612 3.36734694 3.46938776 3.57142857
 3.67346939 3.7755102  3.87755102 3.97959184 4.08163265 4.18367347
 4.28571429 4.3877551  4.48979592 4.59183673 4.69387755 4.79591837
 4.89795918 5.        ]

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 = chi.pdf(x, 1, 6)
y2 = chi.pdf(x, 1, 4)
plt.plot(x, y1, "*", x, y2, "r--")

输出 ：