scipy stats.chi() | Python (original) (raw)

Last Updated : 20 Mar, 2019

scipy.stats.chi() is an chi continuous random variable that is defined with a standard format and some shape parameters to complete its specification.

Parameters : q : lower and upper tail probability x : quantilesloc : [optional] location parameter. Default = 0scale : [optional] scale parameter. Default = 1size : [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) = halfnormal
• chi(2, 0, scale) = rayleigh
• chi(3, 0, scale) : maxwell

Code #1 : Creating chi continuous random variable

Python3 `

importing scipy

from scipy.stats import chi

numargs = chi.numargs [a] = [0.6, ] * numargs rv = chi(a)

print ("RV : \n", rv)

`

Output :

RV : <scipy.stats._distn_infrastructure.rv_frozen object at 0x000002948537C6D8>

Code #2 : chi random variates and probability distribution.

Python3 1== `

import numpy as np quantile = np.arange (0.01, 1, 0.1)

Random Variates

R = chi.rvs(a, scale = 2, size = 10) print ("Random Variates : \n", R)

PDF

R = chi.pdf(a, quantile, loc = 0, scale = 1) print ("\nProbability Distribution : \n", R)

`

Output :

Random Variates : [2.40483665 1.68478304 0.01664071 2.48977805 3.66286843 1.68463842 0.14434643 0.67812242 0.46190886 1.99973997]

Probability Distribution : [0.01384193 0.14349716 0.25719966 0.35519439 0.43801475 0.50641521 0.56131243 0.60373433 0.63477687 0.65556791]

Code #3 : Graphical Representation.

Python3 `

import numpy as np import matplotlib.pyplot as plt

distribution = np.linspace(0, np.minimum(rv.dist.b, 5)) print("Distribution : \n", 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 : Varying Positional Arguments

Python3 1== `

import matplotlib.pyplot as plt import numpy as np

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

Varying positional arguments

y1 = chi.pdf(x, 1, 6) y2 = chi.pdf(x, 1, 4) plt.plot(x, y1, "*", x, y2, "r--")

`

Output :