SnailJumper/evolution.py at master · mohamadch91/SnailJumper · GitHub

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import copy
import random
from player import Player
import numpy as np


class Evolution:
    def __init__(self):
        self.game_mode = "Neuroevolution"

    def next_population_selection(self, players, num_players):
        """
        Gets list of previous and current players (μ + λ) and returns num_players number of players based on their
        fitness value.

        :param players: list of players in the previous generation
        :param num_players: number of players that we return
        :returns player who selected with SUS algorithm
        """

        best=players[0].fitness
        worst=players[0].fitness
        avg=0
        for i in players:
            if(best<i.fitness):
                best=i.fitness
            if(worst>i.fitness):
                worst=i.fitness
            avg+=i.fitness
        avg/=len(players)
        f=open("avrage.txt",'a')
        f.write(str(best)+" ")
        f.write(str(worst)+" ")
        f.write(str(avg)+" ")
        f.write("\n")
        f.close()
        # first create coppy of player
        players_copy = []
        for i in players:
            players_copy.append(i)
        new_players = []
        # #implement SUS
        # players_fittnes=[]
        # players_sorted=sorted(players_copy,key=lambda x:x.fitness)
        # fitness_sum=0
        # for player in players_sorted:
        #     fitness_sum+=player.fitness
        # for player in players_sorted:
        #     players_fittnes.append(player.fitness/fitness_sum)
        # random_num=random.uniform(0,1/num_players)
        # sum=players_fittnes[0]
        # count=0
        # for counter in range(num_players):
        #     while((random_num>sum)):
        #         count+=1
        #         sum+=players_fittnes[count]
        #     new_players.append(players_sorted[count])
        #     random_num+=1/num_players

        # implement Q tournament
        for i in range(num_players):
            temp_players = []
            for j in range(3):
                x = random.randint(0, len(players_copy) - 1)
                temp_players.append(players_copy[x])
            best_fitness = temp_players[0]
            for player in temp_players:
                if (player.fitness > best_fitness.fitness):
                    best_fitness = player
            new_players.append(best_fitness)


        return new_players

    def generate_new_population(self, num_players, prev_players=None):
        """
        Gets survivors and returns a list containing num_players number of children.

        :param num_players: Length of returning list
        :param prev_players: List of survivors
        :return: A list of children
        """
        first_generation = prev_players is None
        if first_generation:
            return [Player(self.game_mode) for _ in range(num_players)]
        else:
            new_parents = []
            # TODO ( Parent selection and child generation )
            # TODO ( Select  parent with Q Tournament )
            # sus
            players_fittnes = []
            players_sorted = sorted(prev_players, key=lambda x: x.fitness)
            fitness_sum = 0
            for player in players_sorted:
                fitness_sum += player.fitness
            for player in players_sorted:
                players_fittnes.append(player.fitness / fitness_sum)
            random_num = random.uniform(0, 1 / num_players)
            sum = players_fittnes[0]
            count = 0
            for counter in range(num_players):
                while ((random_num > sum)):
                    count += 1
                    sum += players_fittnes[count]
                new_parents.append(players_sorted[count])
                random_num += 1 / num_players

            # # q tournumant
            #      for i in range(num_players):
            #          temp_players=[]
            #          for j in range(3):
            #              x=random.randint(0,len(prev_players)-1)
            #              # print(x)
            #              temp_players.append(prev_players[x])
            #          best_fitness=temp_players[0]
            #          for player in temp_players:
            #              if(player.fitness>best_fitness.fitness):
            #                  best_fitness=player
            #          new_parents.append(best_fitness)
            children = []
            ranger = int(num_players / 2)
            for j in range(ranger):
                parent1 = random.randint(0, len(new_parents) - 1)
                parent2 = random.randint(0, len(new_parents) - 1)

                while (parent1 == parent2):
                    parent2 = random.randint(0, len(new_parents) - 1)
                parent1 = new_parents[parent1]
                parent2 = new_parents[parent2]
                children1 = self.clone_player(parent1)
                children2 = self.clone_player(parent2)
                for i in range(10):
                    children1.nn.w[0][i] = parent2.nn.w[0][i]
                    children2.nn.w[0][i] = parent1.nn.w[0][i]
                for m in reversed(range(11, 20)):
                    children1.nn.w[0][m] = parent2.nn.w[0][m]
                    children2.nn.w[0][m] = parent1.nn.w[0][m]
                # mutation
                children1, children2 = self.mutate(children1, children2, 3)
                children1, children2 = self.mutate(children1, children2, 7)
                children1, children2 = self.mutate(children1, children2, 14)
                children1, children2 = self.mutate(children1, children2, 17)
                children1, children2 = self.mutate(children1, children2, 19)
                children.append(children1)
                children.append(children2)
            # new_players = prev_players  # DELETE THIS AFTER YOUR IMPLEMENTATION
            return children

    def mutate(self, children1, children2, k):
        for counter in range(5):
            children1.nn.w[0][k][counter] = random.uniform(0, 1)
            children2.nn.w[0][k][counter] = random.uniform(0, 1)
        return children1, children2

    def clone_player(self, player):
        """
        Gets a player as an input and produces a clone of that player.
        """
        new_player = Player(self.game_mode)
        new_player.nn = copy.deepcopy(player.nn)
        new_player.fitness = player.fitness
        return new_player