scipy stats.gompertz () | 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: Gompertz (or truncated Gumbel) continuous random variable

Code # 1 : Generating a continuous Gompertz (or truncated Gumbel) random variable

from scipy.stats import gompertz 

 

numargs = gompertz.numargs

[a] = [ 0.7 ,] * numargs

rv = gompertz (a)

  

print ( " RV: " , rv) 

Output:

 RV: & lt; scipy.stats._distn_infrastructure.rv_frozen object at 0x000001E39A3E2470 & gt; 

Code # 2: Gompertz (or Truncated Gumbel) Random Variations and Probability Distribution

import numpy as np

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

 
# Random Variants

R = gompertz.rvs (a, scale = 2 , size = 10 )

print ( "Random Variates:" , R)

 
# PDF

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

print ( "Probability Distribution:" , R)

Output:

 Random Variates: [1.29938059 1.47547887 1.33324567 1.79424061 0.45304378 1.46222247 1.29260365 0.59989705 3.58467676 1.81226267] Probability Distribution: [0.01993441 0.19813875 0.34179784 0.45591617 0.54485437 0.61240 685 0.66187043 0.69610503 0.71758726 0.72845776] 

Code # 3: Graphic representation.

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 (distr ibution, rv.pdf (distribution))

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 matplotlib. pyplot as plt

import numpy as np

 

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

 
# Various positional arguments

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

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

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

Output:





Get Solution for free from DataCamp guru