scipy stats.gilbrat () | python

NumPy | Python Methods and Functions

Parameters:
- & gt; q: lower and upper tail probability
- & gt; x: quantiles
- & gt; loc: [optional] location parameter. Default = 0
- & gt; scale: [optional] scale parameter. Default = 1
- & gt; size: [tuple of ints, optional] shape or random variates.
- & gt; moments: [optional] composed of letters [`mvsk`]; `m` = mean, `v` = variance, `s` = Fisher`s skew and `k` = Fisher`s kurtosis. (default = `mv`).

Results: Gilbrat continuous random variable

Code # 1: Generating a continuous random variable Gilbrat

from scipy.stats import gilbrat 

 

numargs = gilbrat .numargs

[] = [ 0.7 ,] * numargs

rv = gilbrat ()

  

print ( " RV: " , rv) 

Output:

 RV: & lt; scipy.stats._distn_infrastructure.rv_frozen object at 0x000001E39A3B4AC8 & gt; 

Code # 2: Gilbrat random variables and probability distribution

import numpy as np

import numpy as np

quantile = np.arange ( 0.01 , 1 , 0.1 )

 
# Random Variants

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

print ( "Random Variates:" , R)

 
# PDF

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

print ( "Probability Distribution: " , R)

Output:

 Random Variates: [0.66090031 1.39027118 1.33876164 1.50366592 5.21419497 5.24225463 3.98547687 0.30586938 9.11346685 0.93014057] Probability Distribution: [0.00099024 0.31736749 0.5620854 0.64817773 0.65389139 0.62357239 0.57879516 0.52988354 0.48170703 0.43645277] 

Code # 3: Graphic representation

Output:

 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. ] 

Code # 4: Various Positional Arguments

import numpy as np

import matplotlib.pyplot as plt

 

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

print ( " Distribution: " , distribution)

 

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

import matplotlib. pyplot as plt

import numpy as np

 

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

 
# Various positional arguments

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

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

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

Output:





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