Python Scipy stats.gengamma()用法及代码示例

scipy.stats.gengamma()是广义的伽玛连续随机变量，使用标准格式和一些形状参数定义以完成其规格。

参数：
-> q :上下尾概率
-> x :分位数
-> loc :[可选]位置参数。默认值= 0
-> scale:[可选]比例参数。默认值= 1
-> size :[int型元组，可选]形状或随机变量。
-> a，b：形状参数
-> moments:[可选]由字母['mvsk']组成； “ m” =均值，“ v” =方差，“ s” = Fisher的偏度，“ k” = Fisher的峰度。 (默认=“ MV”)。

Results:广义伽玛连续随机变量

代码1：创建广义伽玛连续随机变量

from scipy.stats import gengamma  
  
numargs = gengamma .numargs 
[a, b] = [0.7, ] * numargs 
rv = gengamma (a, b) 
  
print ("RV:\n", rv) 

输出：

RV:
 <scipy.stats._distn_infrastructure.rv_frozen object at 0x000001E39A2A8BE0>

代码2：广义伽玛随机变量和概率分布

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

输出：

Random Variates:
 [1.28899567e-01 6.07031120e-06 7.58807426e-01 1.02689244e+00
 2.75752340e-02 8.07943863e-03 4.69774065e-01 2.48110421e-01
 4.64544740e-01 7.04892852e+00]

Probability Distribution:
 [0.004053   0.04502864 0.08671695 0.12897998 0.17168235 0.21469197
 0.25788056 0.30112428 0.34430406 0.38730608]

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：更改位置参数

import matplotlib.pyplot as plt 
import numpy as np 
  
x = np.linspace(0, 5, 100) 
  
# Varying positional arguments 
y1 = gengamma.pdf(x, a, 1, 3) 
y2 = gengamma.pdf(x, a, 1, 4) 
plt.plot(x, y1, "*", x, y2, "r--")

输出：