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Python – Von Mises Line Distribution in Statistics

scipy.stats.vonmises_line() is a Von Mises continuous random variable. It is inherited from the of generic methods as an instance of the rv_continuous class. It completes the methods with details specific for this particular distribution.

Parameters :

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.
moments : [optional] composed of letters [‘mvsk’]; ‘m’ = mean, ‘v’ = variance, ‘s’ = Fisher’s skew and ‘k’ = Fisher’s kurtosis. (default = ‘mv’).

Results : Von Mises continuous random variable

Code #1 : Creating Von Mises continuous random variable




# importing library
  
from scipy.stats import vonmises_line 
    
numargs = vonmises_line .numargs 
a, b = 0.2, 0.8
rv = vonmises_line (a, b) 
    
print ("RV : \n", rv)  


Output :

RV : 
 scipy.stats._distn_infrastructure.rv_frozen object at 0x000002A9D846DB08

Code #2 : Von Mises continuous variates and probability distribution




import numpy as np 
quantile = np.arange (0.01, 1, 0.1
  
# Random Variates 
R = vonmises_line .rvs(a, b, size = 10
print ("Random Variates : \n", R) 
  
# PDF 
x = np.linspace(vonmises_line.ppf(0.01, a, b),
                vonmises_line.ppf(0.99, a, b), 10)
R = vonmises_line.pdf(x, 1, 3)
print ("\nProbability Distribution : \n", R) 


Output :

Random Variates : 
 [ 2.89270823  2.8915421  -1.76479802 -1.18770681  2.16361999  0.78737803
  1.15739329  2.77852158  3.37326365  0.76369761]

Probability Distribution : 
 [0.         0.         0.         0.         0.05510669 0.09455777
 0.18422368 0.30266756 0.33604068 0.24063755]

Code #3 : Graphical Representation.




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)) 


Output :

Distribution : 
 [0.         0.04081633 0.08163265 0.12244898 0.16326531 0.20408163
 0.24489796 0.28571429 0.32653061 0.36734694 0.40816327 0.44897959
 0.48979592 0.53061224 0.57142857 0.6122449  0.65306122 0.69387755
 0.73469388 0.7755102  0.81632653 0.85714286 0.89795918 0.93877551
 0.97959184 1.02040816 1.06122449 1.10204082 1.14285714 1.18367347
 1.2244898  1.26530612 1.30612245 1.34693878 1.3877551  1.42857143
 1.46938776 1.51020408 1.55102041 1.59183673 1.63265306 1.67346939
 1.71428571 1.75510204 1.79591837 1.83673469 1.87755102 1.91836735
 1.95918367 2.        ]
  

Code #4 : Varying Positional Arguments




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


Output :

Dominic Rubhabha-Wardslaus
Dominic Rubhabha-Wardslaushttp://wardslaus.com
infosec,malicious & dos attacks generator, boot rom exploit philanthropist , wild hacker , game developer,
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