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: "scipy.stats._distn_infrastructure.rv_frozen object at 0x000002948537C6D8" 

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: