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ag.py
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ag.py
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import random
import numpy as np
class Genoma():
fitness = 0
def __init__(self):
pass
def start_população(tamanho):
genomas = []
for i in range(tamanho):
genomas.append(Genoma())
return genomas
melhores = [0,0]
def selecao(genomas):
global melhores
genomas1 = genomas
genomas = sorted(genomas)
melhor1 = max(genomas)
melhores[0] = melhor1
# if melhores[0] >= melhores[1]:
# pai1 = genomas.index(melhores[0])
# melhores[1] = melhores[0]
# else:
# pai1 = genomas.index(melhores[1])
# if np.isclose(a = max(genomas), b = genomas1[-1], atol=3):
# pai2 = genomas1.index(genomas1[-1])
# pai2 = pai2 - random.randint(0, 10)
# pai1 = genomas.index(genomas[random.randrange(0, len(genomas) -1)])
# #print("pegou o aproximado")
# return pai2, pai1
# else:
# pai2 = genomas.index(genomas[random.randrange(0, len(genomas) -1)])
# #print("pegou o aleatorio")
# return pai1, pai2
return genomas.index(melhor1), genomas.index(melhor1)
def cruzamento(rede1, rede2):
pesos = {
'A': [None, None, None],
'B': [None, None, None],
'C': [None, None],
'D': [None, None],
'E': [None, None],
'F': [None, None],
'G': [None, None],
'H': [None, None],
'I': [None, None, None,
None, None, None],
'J': [None, None, None,
None, None, None]
}
corte_bias = random.randrange(0, len(rede1[0]) - 1)
bias = rede1[0][:corte_bias] + rede2[0][corte_bias:]
pesos['A'][0] = rede1[1]['A'][0]
pesos['A'][1] = rede2[1]['A'][1]
pesos['A'][2] = rede1[1]['A'][2]
pesos['B'][0] = rede1[1]['B'][0]
pesos['B'][1] = rede2[1]['B'][1]
pesos['B'][2] = rede1[1]['B'][2]
pesos['C'][0] = rede1[1]['C'][0]
pesos['C'][1] = rede1[1]['C'][1]
pesos['D'][0] = rede2[1]['D'][0]
pesos['D'][1] = rede2[1]['D'][1]
pesos['E'][0] = rede1[1]['E'][0]
pesos['E'][1] = rede1[1]['E'][1]
pesos['F'][0] = rede2[1]['F'][0]
pesos['F'][1] = rede2[1]['F'][1]
pesos['G'][0] = rede1[1]['G'][0]
pesos['G'][1] = rede1[1]['G'][1]
pesos['H'][0] = rede2[1]['H'][0]
pesos['H'][1] = rede2[1]['H'][1]
pesos['I'][0] = rede1[1]['J'][0]
pesos['I'][1] = rede2[1]['J'][1]
pesos['I'][2] = rede1[1]['J'][2]
pesos['I'][3] = rede1[1]['J'][3]
pesos['I'][4] = rede2[1]['J'][4]
pesos['I'][5] = rede1[1]['J'][5]
pesos['J'][0] = rede1[1]['J'][0]
pesos['J'][1] = rede2[1]['J'][1]
pesos['J'][2] = rede1[1]['J'][2]
pesos['J'][3] = rede1[1]['J'][3]
pesos['J'][4] = rede2[1]['J'][4]
pesos['J'][5] = rede1[1]['J'][5]
rede = (bias, pesos)
#print("fez o cruzamento")
return rede
def mutacao(rede, tx_mut):
pesos = {
'A': [None, None, None],
'B': [None, None, None],
'C': [None, None],
'D': [None, None],
'E': [None, None],
'F': [None, None],
'G': [None, None],
'H': [None, None],
'I': [None, None, None,
None, None, None],
'J': [None, None, None,
None, None, None]
}
bias = []
for b in rede[0]:
#b = b * tx_mut
bias.append(b)
if random.random() < tx_mut:
pesos['A'][0] = rede[1]['A'][0] + ((rede[1]['A'][0] * tx_mut) +(rede[1]['A'][0] + random.uniform(-1,1)))
pesos['A'][1] = rede[1]['A'][1] + ((rede[1]['A'][1] * tx_mut) +(rede[1]['A'][1] + random.uniform(-1,1)))
pesos['A'][2] = rede[1]['A'][2] + ((rede[1]['A'][2] * tx_mut) +(rede[1]['A'][2] + random.uniform(-1,1)))
pesos['B'][0] = rede[1]['B'][0] + ((rede[1]['B'][0] * tx_mut) +(rede[1]['B'][0] + random.uniform(-1,1)))
pesos['B'][1] = rede[1]['B'][1] + ((rede[1]['B'][1] * tx_mut) +(rede[1]['B'][1] + random.uniform(-1,1)))
pesos['B'][2] = rede[1]['B'][2] + ((rede[1]['B'][2] * tx_mut) +(rede[1]['B'][2] + random.uniform(-1,1)))
pesos['C'][0] = rede[1]['C'][0] + ((rede[1]['C'][0] * tx_mut) +(rede[1]['C'][0] + random.uniform(-1,1)))
pesos['C'][1] = rede[1]['C'][1] + ((rede[1]['C'][1] * tx_mut) +(rede[1]['C'][1] + random.uniform(-1,1)))
pesos['D'][0] = rede[1]['D'][0] + ((rede[1]['D'][0] * tx_mut) +(rede[1]['D'][0] + random.uniform(-1,1)))
pesos['D'][1] = rede[1]['D'][1] + ((rede[1]['D'][1] * tx_mut) +(rede[1]['D'][1] + random.uniform(-1,1)))
pesos['E'][0] = rede[1]['E'][0] + ((rede[1]['E'][0] * tx_mut) +(rede[1]['E'][0] + random.uniform(-1,1)))
pesos['E'][1] = rede[1]['E'][1] + ((rede[1]['E'][1] * tx_mut) +(rede[1]['E'][1] + random.uniform(-1,1)))
pesos['F'][0] = rede[1]['F'][0] + ((rede[1]['F'][0] * tx_mut) +(rede[1]['F'][0] + random.uniform(-1,1)))
pesos['F'][1] = rede[1]['F'][1] + ((rede[1]['F'][1] * tx_mut) +(rede[1]['F'][1] + random.uniform(-1,1)))
pesos['G'][0] = rede[1]['G'][0] + ((rede[1]['G'][0] * tx_mut) +(rede[1]['G'][0] + random.uniform(-1,1)))
pesos['G'][1] = rede[1]['G'][1] + ((rede[1]['G'][1] * tx_mut) +(rede[1]['G'][1] + random.uniform(-1,1)))
pesos['H'][0] = rede[1]['H'][0] + ((rede[1]['H'][0] * tx_mut) +(rede[1]['H'][0] + random.uniform(-1,1)))
pesos['H'][1] = rede[1]['H'][1] + ((rede[1]['H'][1] * tx_mut) +(rede[1]['H'][1] + random.uniform(-1,1)))
pesos['I'][0] = rede[1]['I'][0] + ((rede[1]['I'][0] * tx_mut) +(rede[1]['I'][0] + random.uniform(-1,1)))
pesos['I'][1] = rede[1]['I'][1] + ((rede[1]['I'][1] * tx_mut) +(rede[1]['I'][1] + random.uniform(-1,1)))
pesos['I'][2] = rede[1]['I'][2] + ((rede[1]['I'][2] * tx_mut) +(rede[1]['I'][2] + random.uniform(-1,1)))
pesos['I'][3] = rede[1]['I'][3] + ((rede[1]['I'][3] * tx_mut) +(rede[1]['I'][3] + random.uniform(-1,1)))
pesos['I'][4] = rede[1]['I'][4] + ((rede[1]['I'][4] * tx_mut) +(rede[1]['I'][4] + random.uniform(-1,1)))
pesos['I'][5] = rede[1]['I'][5] + ((rede[1]['I'][1] * tx_mut) +(rede[1]['I'][1] + random.uniform(-1,1)))
pesos['J'][0] = rede[1]['J'][0] + ((rede[1]['J'][0] * tx_mut) +(rede[1]['J'][0] + random.uniform(-1,1)))
pesos['J'][1] = rede[1]['J'][1] + ((rede[1]['J'][1] * tx_mut) +(rede[1]['J'][1] + random.uniform(-1,1)))
pesos['J'][2] = rede[1]['J'][2] + ((rede[1]['J'][2] * tx_mut) +(rede[1]['J'][2] + random.uniform(-1,1)))
pesos['J'][3] = rede[1]['J'][3] + ((rede[1]['J'][3] * tx_mut) +(rede[1]['J'][3] + random.uniform(-1,1)))
pesos['J'][4] = rede[1]['J'][4] + ((rede[1]['J'][4] * tx_mut) +(rede[1]['J'][4] + random.uniform(-1,1)))
pesos['J'][5] = rede[1]['J'][5] + ((rede[1]['J'][1] * tx_mut) +(rede[1]['J'][1] + random.uniform(-1,1)))
#print("sofreu mutação"
return bias, pesos
else:
return rede[0], rede[1]