#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Tue Jan 17 16:46:22 2017
@author: liupeng
"""
import math
import random
# 神经网络3层, 1个隐藏层; 4个input和1个output
network = [4, [16], 1]
# 遗传算法相关
population = 50
elitism = 0.2
random_behaviour = 0.1
mutation_rate = 0.5
mutation_range = 2
historic = 0
low_historic = False
score_sort = -1
n_child = 1
# 激活函数
def sigmoid(z):
return 1.0/(1.0+math.exp(-z))
# random number
def random_clamped():
return random.random()*2-1
# "神经元"
class Neuron():
def __init__(self):
self.biase = 0
self.weights = []
def init_weights(self, n):
self.weights = []
for i in range(n):
self.weights.append(random_clamped())
def __repr__(self):
return 'Neuron weight size:{} biase value:{}'.format(len(self.weights), self.biase)
# 层
class Layer():
def __init__(self, index):
self.index = index
self.neurons = []
def init_neurons(self, n_neuron, n_input):
self.neurons = []
for i in range(n_neuron):
neuron = Neuron()
neuron.init_weights(n_input)
self.neurons.append(neuron)
def __repr__(self):
return 'Layer ID:{} Layer neuron size:{}'.format(self.index, len(self.neurons))
# 神经网络
class NeuroNetwork():
def __init__(self):
self.layers = []
# input:输入层神经元数 hiddens:隐藏层 output:输出层神经元数
def init_neuro_network(self, input, hiddens , output):
index = 0
previous_neurons = 0
# input
layer = Layer(index)
layer.init_neurons(input, previous_neurons)
previous_neurons = input
self.layers.append(layer)
index += 1
# hiddens
for i in range(len(hiddens)):
layer = Layer(index)
layer.init_neurons(hiddens[i], previous_neurons)
previous_neurons = hiddens[i]
self.layers.append(layer)
index += 1
# output
layer = Layer(index)
layer.init_neurons(output, previous_neurons)
self.layers.append(layer)
def get_weights(self):
data = { 'network':[], 'weights':[] }
for layer in self.layers:
data['network'].append(len(layer.neurons))
for neuron in layer.neurons:
for weight in neuron.weights:
data['weights'].append(weight)
return data
def set_weights(self, data):
previous_neurons = 0
index = 0
index_weights = 0
self.layers = []
for i in data['network']:
layer = Layer(index)
layer.init_neurons(i, previous_neurons)
for j in range(len(layer.neurons)):
for k in range(len(layer.neurons[j].weights)):
layer.neurons[j].weights[k] = data['weights'][index_weights]
index_weights += 1
previous_neurons = i
index += 1
self.layers.append(layer)
# 输入游戏环境中的一些条件(如敌机位置), 返回要执行的操作
def feed_forward(self, inputs):
for i in range(len(inputs)):
self.layers[0].neurons[i].biase = inputs[i]
prev_layer = self.layers[0]
for i in range(len(self.layers)):
# 第一层没有weights
if i == 0:
continue
for j in range(len(self.layers[i].neurons)):
sum = 0
for k in range(len(prev_layer.neurons)):
sum += prev_layer.neurons[k].biase * self.layers[i].neurons[j].weights[k]
self.layers[i].neurons[j].biase = sigmoid(sum)
prev_layer = self.layers[i]
out = []
last_layer = self.layers[-1]
for i in range(len(last_layer.neurons)):
out.append(last_layer.neurons[i].biase)
return out
def print_info(self):
for layer in self.layers:
print(layer)
# "基因组"
class Genome():
def __init__(self, score, network_weights):
self.score = score
self.network_weights = network_weights
class Generation():
def __init__(self):
self.genomes = []
def add_genome(self, genome):
i = 0
for i in range(len(self.genomes)):
if score_sort < 0:
if genome.score > self.genomes[i].score:
break
else:
if genome.score < self.genomes[i].score:
break
self.genomes.insert(i, genome)
# 杂交+突变
def breed(self, genome1, genome2, n_child):
datas = []
for n in range(n_child):
data = genome1
for i in range(len(genome2.network_weights['weights'])):
if random.random() <= 0.5:
data.network_weights['weights'][i] = genome2.network_weights['weights'][i]
for i in range(len(data.network_weights['weights'])):
if random.random() <= mutation_rate:
data.network_weights['weights'][i] += random.random() * mutation_range * 2 - mutation_range
datas.append(data)
return datas
# 生成下一代
def generate_next_generation(self):
nexts = []
for i in range(int(round(elitism*population))):
if len(nexts) < population:
nexts.append(self.genomes[i].network_weights)
for i in range(int(round(random_behaviour*population))):
n = self.genomes[0].network_weights
for k in range(len(n['weights'])):
n['weights'][k] = random_clamped()
if len(nexts) < population:
nexts.append(n)
max_n = 0
while True:
for i in range(max_n):
childs = self.breed(self.genomes[i], self.genomes[max_n], n_child if n_child > 0 else 1)
for c in range(len(childs)):
nexts.append(childs[c].network_weights)
if len(nexts) >= population:
return nexts
max_n += 1
if max_n >= len(self.genomes)-1:
max_n = 0
class Generations():
def __init__(self):
self.generations = []
def first_generation(self):
out = []
for i in range(population):
nn = NeuroNetwork()
nn.init_neuro_network(network[0], network[1], network[2])
out.append(nn.get_weights())
self.generations.append(Generation())
return out
def next_generation(self):
if len(self.generations) == 0:
return False
gen = self.generations[-1].generate_next_generation()
self.generations.append(Generation())
return gen
def add_genome(self, genome):
if len(self.generations) == 0:
return False
return self.generations[-1].add_genome(genome)
class NeuroEvolution():
def __init__(self):
self.generations = Generations()
def restart(self):
self.generations = Generations()
def next_generation(self):
networks = []
if len(self.generations.generations) == 0:
networks = self.generations.first_generation()
else:
networks = self.generations.next_generation()
nn = []
for i in range(len(networks)):
n = NeuroNetwork()
n.set_weigh
