Python – 统计中的 Kolmogorov-Smirnov 分布
scipy.stats.kstwobign()是用于大 N 测试的 Kolmogorov-Smirnov 双边测试,它使用标准格式和一些形状参数定义以完成其规范。这是一种统计测试,用于测量理论 CDF 与经验 CDF 的最大绝对距离。
参数 :
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.
Results : kstwobign continuous random variable
代码 #1:创建 kstwobign 连续随机变量
# importing library
from scipy.stats import kstwobign
numargs = kstwobign.numargs
a, b = 4.32, 3.18
rv = kstwobign(a, b)
print ("RV : \n", rv)
输出 :
RV :
scipy.stats._distn_infrastructure.rv_frozen object at 0x000002A9D54959C8
代码#2:kstwobign 连续变量和概率分布
import numpy as np
quantile = np.arange (0.01, 1, 0.1)
# Random Variates
R = kstwobign.rvs(a, b, scale = 2, size = 10)
print ("Random Variates : \n", R)
输出 :
Random Variates :
[3.88510141 3.48394857 3.66124797 3.88484201 3.86533511 3.21176073
4.10238585 3.42397866 3.85111721 4.36433596]
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: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 = kstwobign .pdf(x, 1, 3)
y2 = kstwobign .pdf(x, 1, 4)
plt.plot(x, y1, "*", x, y2, "r--")
输出 :