pplns_estimator.py

import random


def get_reward(shares):
    return shares[0] / float(1+sum(shares[1:]))

def run_sim(low, high, N):
	# preload the share chain
	shares = [random.randint(low, high) for i in range(N)]

	# add shares and sum the expected revenue from each
	revenue = 0.
	for i in range(N):
		revenue += get_reward(shares)
		shares.pop()
		shares.insert(0, random.randint(low, high))

	return revenue

if __name__ == "__main__":
	for i in range(10):
		print run_sim(1,10,2016)