1. Python
  2. scipy stats.chi () | python

scipy stats.chi () | python



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: chi continuous random variable

Special cases:

  • chi (1, loc, scale) = full normal
  • chi (2, 0, scale) = rayleigh
  • Chi (3, 0, scale): Maxwell

Code # 1: Generating a continuous random Chi

# scipy import

from scipy.stats import chi 

 

numargs = chi. numargs

[a] = [ 0.6 ,] * numargs

rv = chi (a)

 

print ( "RV:" , rv) 

Exit: 

 RV: & lt; scipy.stats._distn_infrastructure.rv_frozen object at 0x000002948537C6D8 & gt; 

Code # 2: Random Variations and Probability Distribution.

import numpy as np

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

 
# Random Variants

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

print ( "Random Variates:" , R)

 
# PDF

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

print ( "Probability Distribution:" , R)

Output: 

 Random Variates: [2.40483665 1.68478304 0.01664071 2.48977805 3.66286843 1.68463842 0.14434643 0.67812242 0.46190886 1.99973997] Probability193 0.14 0.25719966 0.35519439 0.43801475 0.50641521 0.56131243 0.60373433 0.63477687 0.6555 6791]

Code # 3: Graphic representation.

import numpy as np

import matplotlib.pyplot as plt

 

distribution = np.linspace ( 0 , np.minimum (rv.dist.b, 5 ))

print ( "Distribution:" , distribution)

 

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

 

Output: 

 Distribution: Distribution: [0. 0.10204082 0.20408163 0.30612245 0.40816327 0.51020408 0.6122449 0.71428571 0.81632653 0.91836735 1.02040816 1.12244898 1.2244898 1.32653061 1.42857143 1.53061224 1.63265306 1.73469388 1.83673469 1.93877551 2.04081633 2.14285714 2.24489796 2.34693878 2.44897959 2.55102041 2.65306122 2.75510204 2.85714286 2.95918367 3.06122449 3.16326531 3.26530612 3.36734694 3.46938776 3.57142857 3.67346939 3.7755102 3.87755102 3.97959184 4.08163265 4.18367347 4.28571429 4.3877551 4.48979592 4.59183673 4.69387755 4.79591837 4.89795918 5. ]

Code # 4: Various Positional Arguments

import matplotlib. pyplot as plt

import numpy as np

 

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

 
# Various positional arguments

y1 = chi.pdf (x, 1 , 6 )

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

plt.plot (x, y1, " * " < code class = "plain">, x, y2, "r--" )

Output: