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evolution.py
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evolution.py
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import copy
import json
import random
from random import choice, randint, seed
import numpy as np
from numpy import shape
from player import Player
def get_fitness(elem):
return elem.fitness
class Evolution:
def __init__(self):
self.game_mode = "Neuroevolution"
self.write = False
def roulette_wheel(self, items, num_items):
probabilities = self.get_probability_list(items)
chosen = []
for n in range(num_items):
r = random.random()
for (i, individual) in enumerate(items):
if r <= probabilities[i]:
chosen.append(individual)
break
return chosen
def sus(self, items, num_items):
points = self.generate_points(items, num_items)
chosen = []
while len(chosen) < num_items:
random.shuffle(items)
i = 0
while i < len(points) and len(chosen) < num_items:
j = 0
sm = 0
while j < len(items):
sm += items[j].fitness
if sm > points[i]:
chosen.append(items[j])
break
j += 1
i += 1
return chosen
def top_k(self, items, num_items, k=2):
chosen = []
for iteration in range(num_items):
best = None
for i in range(k):
r = randint(0, len(items) - 1)
if best is None or (items[r].fitness > items[best].fitness):
best = r
chosen.append(items[best])
return chosen
@staticmethod
def generate_points(items, num_items):
total_fitness = float(sum([item.fitness for item in items]))
point_distance = total_fitness / num_items
start_point = random.uniform(0, point_distance)
points = [start_point + i * point_distance for i in range(num_items)]
return points
def next_population_selection(self, players, num_players, file_to_write, type_of_selection='sort'):
"""
Gets list of previous and current players (μ + λ) and returns num_players number of players based on their
fitness value.
:param type_of_selection: type of selection(top-k, roulette wheel, SUS, sort)
:param players: list of players in the previous generation
:param num_players: number of players that we return
"""
copy_players = [self.clone_player(player) for player in players]
ret = copy_players
if type_of_selection == 'sort':
copy_players.sort(key=get_fitness, reverse=True)
ret = copy_players
if type_of_selection == 'roulette wheel':
ret = self.roulette_wheel(copy_players, num_players)
elif type_of_selection == 'SUS':
ret = self.sus(copy_players, num_players)
elif type_of_selection == 'top-k':
ret = self.top_k(copy_players, num_items=num_players, k=len(copy_players) // 2)
temp = (sorted(ret, key=get_fitness, reverse=True))
sm_fitness = sum([pl.fitness for pl in ret])
if self.write:
file_to_write.write(',' + str([temp[0].fitness, temp[len(temp) - 1].fitness, sm_fitness / num_players]))
else:
file_to_write.write(str([temp[0].fitness, temp[len(temp) - 1].fitness, sm_fitness / num_players]))
self.write = True
return ret
def generate_new_population(self, num_players, prev_players=None, type_of_selection='random'):
"""
Gets survivors and returns a list containing num_players number of children.
:param type_of_selection: type of parent selection(top-k, roulette wheel, SUS, random)
: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:
ret_players = [Player(self.game_mode) for _ in range(num_players)]
return ret_players
else:
print(str(max(prev_players, key=get_fitness).fitness) + '\n')
# TODO ( Parent selection and child generation )
new_players = []
for iteration in range(num_players):
par_a, par_b = self.select_parents(prev_players, type_of_selection=type_of_selection)
child_a, child_b = self.generate_children(par_a, par_b)
new_players.append(child_a)
new_players.append(child_b)
return new_players
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
def select_parents(self, prev_players, type_of_selection='random'):
par_a = choice(prev_players)
par_b = choice(prev_players)
if type_of_selection == 'roulette wheel':
par_a, par_b = self.roulette_wheel(prev_players, 2)
elif type_of_selection == 'SUS':
par_a, par_b = self.sus(prev_players, 2)
elif type_of_selection == 'top-k':
par_a, par_b = self.top_k(prev_players, 2, k=20)
return par_a, par_b
def generate_children(self, par_a, par_b):
value = randint(0, len(par_a.nn.layer_sizes) - 1)
child_a = self.clone_player(par_a)
child_b = self.clone_player(par_b)
for i in range(len(par_a.nn.layer_sizes) - 1):
num = i + 1
shape_1 = par_a.nn.layer_sizes[i + 1]
value = randint(0, shape_1)
W_b = np.concatenate((par_a.nn.parameters['W' + str(num)][:value, :],
par_b.nn.parameters['W' + str(num)][value:, :]), axis=0)
W_a = np.concatenate((par_b.nn.parameters['W' + str(num)][:value, :],
par_a.nn.parameters['W' + str(num)][value:, :]), axis=0)
B_a = np.concatenate((par_a.nn.parameters['b' + str(num)][:value, :],
par_b.nn.parameters['b' + str(num)][value:, :]), axis=0)
B_b = np.concatenate((par_b.nn.parameters['b' + str(num)][:value, :],
par_a.nn.parameters['b' + str(num)][value:, :]), axis=0)
params_a = {'W': W_b, 'b': B_b}
params_b = {'W': W_a, 'b': B_a}
child_a.nn.change_layer_parameters(new_layer_parameters=params_a, layer_num=num)
child_b.nn.change_layer_parameters(new_layer_parameters=params_b, layer_num=num)
child_a.fitness = 0
child_b.fitness = 0
self.mutate(child_a)
self.mutate(child_b)
return child_a, child_b
@staticmethod
def mutate(child):
for i in range(1, len(child.nn.layer_sizes)):
val = randint(0, 100)
if val > 70:
params = {'W': np.random.normal(size=(child.nn.layer_sizes[i], child.nn.layer_sizes[i - 1])),
'b': np.zeros((child.nn.layer_sizes[i], 1))}
child.nn.change_layer_parameters(new_layer_parameters=params, layer_num=i)
@staticmethod
def get_probability_list(items):
fitness = [item.fitness for item in items]
total_fit = float(sum(fitness))
relative_fitness = [f / total_fit for f in fitness]
probabilities = [sum(relative_fitness[:i + 1])
for i in range(len(relative_fitness))]
return probabilities