Python–统计中的逆高斯分布
scipy.stats.invgauss() 是一个反高斯连续随机变量。它继承自泛型方法的，作为 rv_continuous 类的实例。它用特定于这个特定分布的细节来完成这些方法。
参数:
a : 形参 T3】c:耿氏特例。默认值等于 c = -1
代码#1:创建逆高斯连续随机变量
# importing library
from scipy.stats import invgauss
numargs = invgauss.numargs
[a, b] = [0.7, 0.4] * numargs
rv = invgauss (a, b)
print ("RV : \n", rv)
输出:
RV :
scipy.stats._distn_infrastructure.rv_frozen object at 0x1a220d7bd0
代码#2:逆高斯连续变量和概率分布
import numpy as np
quantile = np.arange (0.01, 1)
# Random Variates
R = invgauss.ppf(0.01, a)
print ("Random Variates : \n", R)
# PDF
R = invgauss.pdf(invgauss.ppf(0.01, a), a)
print ("\nProbability Distribution : \n", R)
输出:
Random Variates :
0.25801533159920903
Probability Distribution :
0.15984442779701688
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:变化的位置参数
import matplotlib.pyplot as plt
import numpy as np
x = np.linspace(0, 5, 100)
# Varying positional arguments
y1 = invgauss .pdf(x, 1, 3)
y2 = invgauss .pdf(x, 1, 4)
plt.plot(x, y1, "*", x, y2, "r--")
输出:
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