scipy stats.gilbrat() | Python

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參數:
-> q:上下尾概率
-> x: 分位數
- > loc: [可選] 位置參數。默認 = 0
- > scale: [可選] 比例參數。默認值 = 1
- >大小: [整數元組,可選] 形狀或隨機變量。
- >時刻: [可選] 由字母 [`mvsk`] 組成; `m` = 均值,`v` = 方差,`s` = Fisher`s skew 和`k` = Fisher`s kurtosis。 (默認 = `mv`)。

結果: Gilbrat 連續隨機變量

代碼#1:生成連續隨機變量變量 Gilbrat


from scipy.stats import gilbrat


numargs = gilbrat .numargs

[] = [ 0.7 ,] * numargs

rv = gilbrat ( )

打印 ( " RV:" , rv)

輸出:

RV: 

scipy.stats._distn_infrastructure.rv_frozen 對象位於 0x000001E39A3B4AC8 >

代碼#2:Gilbrat 隨機變量和概率分佈


import numpy as np

import numpy as np

分位數 = np.arange( 0.01 , 1 , 0.1 )


# Random Variants

R = gilbrat.rvs (scale = 2 , size = 10 )

print ( "隨機變量:" , R)


# PDF

R = gilbrat.pdf (quantile, loc = 0 , scale = 1 )

print ( "概率分佈:" , R)

輸出:

隨機分佈隨機數:[0.66090031 1.39027118 1.33876164 1.50366592 5.21419497 5.24225463 3.98547687 0.30586938 9.11346685 0.93014057]概率分佈:[0.00099024 0.31736749 0.5620854 0.64817773 0.65389139 0.6 2357239 0.57879516 0.52988354 0.48170703 0.43645277] 

代碼#3:圖形表示


import numpy as np

import matplotlib.pyplot as plt


分佈 = np .linspace ( 0 , np.minimum (rv.dist.b, 3 ))

打印 ( " 分佈:" ,分佈)


plot = plt.plot (distribution, rv.pdf (distribution))

輸出t:

分佈:[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。 ] 

代碼#4:各種位置參數


import matplotlib. pyplot as plt

import numpy as np


x = np.linspace ( 0 , 5 , 100 )


# 各種位置參數

y1 = gilbrat.pdf (x, 1 , 3 )

y2 = gilbrat.pdf (x , 1 , 4 )

plt.plot(x, y1, " * " , x, y2, " r-- " )

輸出:

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