main.py

import random
import numpy as np
import tensorflow
import matplotlib.pyplot as plt
import pandas as pd


class Student:
    def __init__(self, choice):
        self.choice = choice
        self.policy_class = 0


student_list = []


class PolicyClassroom:
    def __init__(self, class_num):
        self.student = np.zeros(20)
        self.class_num = class_num

    def add_student(self, choice):
        self.student[choice] += 1

    def diversity(self):
        sum = 0.0
        for choice in self.student:
            sum += choice
        diversity = 0.0
        for choice in self.student:
            diversity += choice ** 2 / sum ** 2
        return diversity * 10000


class LearningClassroom:
    def __init__(self):
        pass


class QLearning:

    def __init__(self):
        pass


class1 = PolicyClassroom(1)
diversity_list = []
choice_list = np.zeros(20)
for i in range(1, 40):
    # choice = random.randint(1, 20) - 1
    choice = random.randint(0, 19)
    choice_list[choice] += 1
    class1.add_student(choice)
    diversity_list.append(class1.diversity())
    print(class1.diversity())

plt.subplot(2, 1, 1)
# add a plt for choice diversity
plt.plot(np.linspace(0, 39, 39), diversity_list)
plt.title('Diversity')

labels = 'Frogs', 'Hogs', 'Dogs', 'Logs'
sizes = [15, 30, 45, 10]

plt.subplot(2, 1, 2)
plt.pie(choice_list, autopct='%1.1f%%',
        shadow=True, startangle=90)
plt.axis('equal')

plt.show()

df = pd.DataFrame(choice_list)
print(df)