Python–统计学中的离散几何分布
scipy.stats.geom() 是一个几何离散随机变量。它继承自泛型方法的，作为 rv_discrete 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:
x : 分位数 loc : 【可选】位置参数。默认= 0 刻度:【可选】刻度参数。默认= 1 时刻:【可选】由字母['mvsk']组成；m’=均值，‘v’=方差，‘s’= Fisher 偏斜度，‘k’= Fisher 峰度。(默认值= 'mv ')。
结果:几何离散随机变量
代码#1:创建几何离散随机变量
# importing library
from scipy.stats import geom
numargs = geom .numargs
a, b = 0.2, 0.8
rv = geom (a, b)
print ("RV : \n", rv)
输出:
RV :
scipy.stats._distn_infrastructure.rv_frozen object at 0x0000016A4C37A988
代码#2:几何离散变量和概率分布
import numpy as np
quantile = np.arange (0.01, 1, 0.1)
# Random Variates
R = geom .rvs(a, b, size = 10)
print ("Random Variates : \n", R)
# PDF
x = np.linspace(geom.ppf(0.01, a, b),
geom.ppf(0.99, a, b), 10)
R = geom.ppf(x, 1, 3)
print ("\nProbability Distribution : \n", R)
输出:
Random Variates :
[5 1 1 2 7 9 3 2 1 3]
Probability Distribution :
[nan nan nan nan nan nan nan nan nan nan]
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, 2))
print("Distribution : \n", distribution)
plot = plt.plot(distribution, rv.ppf(distribution))
输出:
Distribution :
[0\. 0.04081633 0.08163265 0.12244898 0.16326531 0.20408163
0.24489796 0.28571429 0.32653061 0.36734694 0.40816327 0.44897959
0.48979592 0.53061224 0.57142857 0.6122449 0.65306122 0.69387755
0.73469388 0.7755102 0.81632653 0.85714286 0.89795918 0.93877551
0.97959184 1.02040816 1.06122449 1.10204082 1.14285714 1.18367347
1.2244898 1.26530612 1.30612245 1.34693878 1.3877551 1.42857143
1.46938776 1.51020408 1.55102041 1.59183673 1.63265306 1.67346939
1.71428571 1.75510204 1.79591837 1.83673469 1.87755102 1.91836735
1.95918367 2\. ]
代码#4:不同的位置参数
import matplotlib.pyplot as plt
import numpy as np
x = np.linspace(0, 5, 100)
# Varying positional arguments
y1 = geom.ppf(x, a, b)
y2 = geom.pmf(x, a, b)
plt.plot(x, y1, "*", x, y2, "r--")
输出:
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