scipy.stats.chi2() is an chi square 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 : 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 squared continuous random variable
Code #1 : Creating chi squared continuous random variable
# importing scipy from scipy.stats import chi2 numargs = chi2.numargs [a] = [0.6, ] * numargs rv = chi2(a) print ("RV : \n", rv) |
Output :
RV : <scipy.stats._distn_infrastructure.rv_frozen object at 0x0000029485196DD8>
Code #2 : chi2 random variates and probability distribution function.
import numpy as np quantile = np.arange (0.01, 1, 0.1) # Random Variates R = chi2.rvs(a, scale = 2, size = 10) print ("Random Variates : \n", R) # PDF R = chi2.pdf(a, quantile, loc = 0, scale = 1) print ("\nProbability Distribution : \n", R) |
Output :
Random Variates : [6.20115012e-01 4.82717678e-01 1.43760444e-02 1.19755537e+00 3.00093606e-05 6.11268950e-01 5.99940774e-01 3.20509994e-01 1.94220599e-01 6.63225404e-01] Probability Distribution : [0.00615404 0.06544849 0.12034254 0.1704933 0.21568622 0.25581903 0.29088625 0.32096438 0.34619796 0.36678666]
Code #3 : Graphical Representation.
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 : [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
import matplotlib.pyplot as plt import numpy as np x = np.linspace(0, 5, 100) # Varying positional arguments y1 = chi2.pdf(x, 1, 6) y2 = chi2.pdf(x, 1, 4) plt.plot(x, y1, "*", x, y2, "r--") |
Output :
