#!/usr/bin/env python

"""

a simple implementation of Apriori algorithm by Python.

"""

import numpy as np

import matplotlib.pyplot as plt

import pandas as pd

# Import dataset

dataset = pd.read_csv('Ads_CTR_Optimisation.csv')

#Implement the Thompson Sampling

import random

N = 10000

d = 10

ads_selected = []

numbers_of_rewards_1 = [0] * d

numbers_of_rewards_0 = [0] * d

total_reward = 0

for n in range(0, N):

ad = 0

max_random = 0

for i in range(0, d):

random_beta = random.betavariate(numbers_of_rewards_1[i] + 1, numbers_of_rewards_0[i] + 1)

if random_beta > max_random:

max_random = random_beta

ad = i

ads_selected.append(ad)

reward = dataset.values[n, ad]

if reward == 1:

numbers_of_rewards_1[ad] = numbers_of_rewards_1[ad] + 1

else:

numbers_of_rewards_0[ad] = numbers_of_rewards_0[ad] + 1

total_reward = total_reward + reward

print("Thompson Sampling: ")

print(total_reward)

plt.hist(ads_selected)

plt.xlabel('Ads')

plt.ylabel('# of times selected')

plt.show()