hof.py

import random
import tensorflow as tf
from matplotlib import pyplot
import os
from math import floor
from model import Model
from agent import Agent
from mnk import Board
from utils import run_game


class HOF:
    def __init__(self, mnk, folder):
        self.hof = []
        self.folder = folder
        self.mnk = mnk
        self.sample_history = []
        self.pop_size = 0
        self.basel = 0  # Used in limit-uniform sampling
        if not os.path.isdir(folder):
            os.makedirs(folder)

    def store(self, model):
        model.save_to("{}/{}".format(self.folder, self.pop_size))
        self.hof.append(self.pop_size)
        self.pop_size += 1
        self.basel += 1 / self.pop_size**2

    # Samples from the hall of fame with the provided method
    def sample(self, method='uniform', index=None):
        if method == 'limit-uniform':  # Performs poorly. Do not use.
            threshold = random.random()*self.basel

            cum_prob = 0
            ind = self.pop_size-1
            for i in range(self.pop_size):
                cum_prob += 1/(self.pop_size-i)**2
                if cum_prob > threshold:
                    ind = i
                    break
        elif method == 'uniform':
            ind = floor(random.random()*self.pop_size)
        elif method == 'naive':
            ind = self.pop_size-1

        if index is not None:
            name = self.hof[index]
        else:
            self.sample_history.append(ind)
            name = self.hof[ind]

        return Model(self.mnk, location="{}/{}".format(self.folder, name))