(Solved): CODING CLUB HOMEWORK 1 import pandas as pd import numpy as np from scipy import stats from sci ...

CODING CLUB HOMEWORK 1 import pandas as pd import numpy as np from scipy import stats from scipy.stats import ks_2samp, poisson \%matplotlib inline import otter grader = otter. Notebook() Question 1: Write a class generic_descrete_png that simulates random integers from any discrete distribution function. The class should accept inputs for the PMF, the size of the simulation array and any additional method arguments can be parsed in from the method itself: For example, i should be able to call your class object and use the simulate method as: generic_descrete_png(binomial_pmf,1000). simulate \( (n=10, p=0.5) \) Do not change or remove anything from the class below, only add! \#\#\#\#\#. Distributions \#\#\#\#\# import math def binomial_pmf \( (n, p, x) \) : return math. \( \operatorname{comb}(n, x) * p^{* *} x *(1-p) * *(n-x) \) def bernoulli_pmf(): \( \cdots \) def poison_pmf \( (x, * * \) kwargs \( ) \) : p.random. seed (seed \( =2212) \) lass generic_descrete_png(): def discrete_cdf(self, \( x \), **wargs): This is a generic discrete CDF which takes the cumulat ive sum of a pmf \( c d f=\theta \) for \( k \) in range \( (x) \) : def discrete_inv_transform_sample(self, p_vec): This function searches over a uniform interval for a subsequent integer for i in range(self.size): realisation = self.discrete_inv_transform_sample(p_vec) while realisation is None: realisation = self.discrete_inv_transform_sample(p_vec) samples.append(realisation)