scipy.stats.bernoulli() is a Bernoulli discrete random variable. It is inherited from the of generic methods as an instance of the rv_discrete class. It completes the methods with details specific for this particular distribution.
Parameters :
x : quantiles
loc : [optional]location parameter. Default = 0
scale : [optional]scale parameter. Default = 1
moments : [optional] composed of letters [‘mvsk’]; ‘m’ = mean, ‘v’ = variance, ‘s’ = Fisher’s skew and ‘k’ = Fisher’s kurtosis. (default = ‘mv’).Results : Bernoulli discrete random variable
Code #1 : Creating Bernoulli discrete random variable
# importing library from scipy.stats import bernoulli numargs = bernoulli .numargs a, b = 0.2, 0.8rv = bernoulli (a, b) print ("RV : \n", rv) |
Output :
RV : scipy.stats._distn_infrastructure.rv_frozen object at 0x0000016A4C0FC108
Code #2 : Bernoulli discrete variates and probability distribution
import numpy as np quantile = np.arange (0.01, 1, 0.1) # Random Variates R = bernoulli .rvs(a, b, size = 10) print ("Random Variates : \n", R) # PDF x = np.linspace(bernoulli.ppf(0.01, a, b), bernoulli.ppf(0.99, a, b), 10) R = bernoulli.ppf(x, 1, 3) print ("\nProbability Distribution : \n", R) |
Output :
Random Variates : [0 0 0 0 0 0 0 0 0 1] Probability Distribution : [ 4. 4. nan nan nan nan nan nan nan nan]
Code #3 : Graphical Representation.
import numpy as np import matplotlib.pyplot as plt distribution = np.linspace(0, np.minimum(rv.dist.b, 2)) print("Distribution : \n", distribution) plot = plt.plot(distribution, rv.ppf(distribution)) |
Output :
Distribution : [0. 0.02040816 0.04081633 0.06122449 0.08163265 0.10204082 0.12244898 0.14285714 0.16326531 0.18367347 0.20408163 0.2244898 0.24489796 0.26530612 0.28571429 0.30612245 0.32653061 0.34693878 0.36734694 0.3877551 0.40816327 0.42857143 0.44897959 0.46938776 0.48979592 0.51020408 0.53061224 0.55102041 0.57142857 0.59183673 0.6122449 0.63265306 0.65306122 0.67346939 0.69387755 0.71428571 0.73469388 0.75510204 0.7755102 0.79591837 0.81632653 0.83673469 0.85714286 0.87755102 0.89795918 0.91836735 0.93877551 0.95918367 0.97959184 1. ]
Code #4 : Varying Positional Arguments
import matplotlib.pyplot as plt import numpy as np x = np.linspace(0, 5, 100) # Varying positional arguments y1 = bernoulli.ppf(x, a, b) y2 = bernoulli.pmf(x, a, b) plt.plot(x, y1, "*", x, y2, "r--") |
Output :
