scipy stats.bradford() | Python
scipy.stats.bradford()是一个 bradford 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 : bradford continuous random variable
代码 #1:创建布拉德福德连续随机变量
# importing scipy
from scipy.stats import bradford
numargs = bradford.numargs
[a] = [0.6, ] * numargs
rv = bradford(a)
print ("RV : \n", rv)
输出 :
RV :
代码 #2: bradford 随机变量和概率分布
import numpy as np
quantile = np.arange (0.01, 1, 0.1)
# Random Variates
R = bradford.rvs(a, scale = 2, size = 10)
print ("Random Variates : \n", R)
# PDF
R = bradford.pdf(quantile, a, loc = 0, scale = 1)
print ("\nProbability Distribution : \n", R)
输出 :
Random Variates :
[0.30727583 0.22129839 0.27130072 0.19795865 1.66069665 1.93938843
0.43435698 0.16437308 0.91592562 1.95369029]
Probability Distribution :
[1.26897205 1.19754774 1.13373525 1.07637933 1.02454726 0.97747771
0.93454311 0.89522152 0.85907529 0.82573473]
代码#3:图形表示。
import numpy as np
import matplotlib.pyplot as plt
distribution = np.linspace(0, np.maximum(rv.dist.b, 5))
print ("Distribution : \n", distribution)
plot = plt.plot(distribution, rv.pdf(distribution))
输出 :
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. ]