Mastering Machine Learning Algorithms

Example of direct sampling

We can now implement this algorithm in Python. Let's start by defining the sample methods using the NumPy function np.random.binomial(1, p), which draws a sample from a Bernoulli distribution with probability p:

import numpy as npdef X1_sample(p=0.35):    return np.random.binomial(1, p)def X2_sample(p=0.65):    return np.random.binomial(1, p)def X3_sample(x1, x2, p1=0.75, p2=0.4):    if x1 == 1 and x2 == 1:        return np.random.binomial(1, p1)    else:        return np.random.binomial(1, p2)    def X4_sample(x3, p1=0.65, p2=0.5):    if x3 == 1:        return np.random.binomial(1, p1)    else:        return np.random.binomial(1, p2)

At this point, we can implement the main cycle. As the variables are Boolean, the total number of probabilities is 16, so we set  ...