def Translate_Into_Networks(input_N, Shape, Output_N, Weight):
network_list = []
path = "gene/"
file_list = os.listdir(path)
gene_file_check = [file for file in file_list if file.endswith(".txt")]
if len(gene_file_check) == 0:
import startup
Gene_List = Genetic.Read_Gene()
for i in range(len(Gene_List)):
network = Network()
Decoded_List = []
Decoded_DNA_List = []
for j in range(len(Gene_List[i])):
Decoded_Gene = Gene_List[i][j].split('-')
if (Decoded_Gene[3] == 'F'):
pass
else:
if Decoded_Gene[1] == '~':
Decoded_List.append(
[int(Decoded_Gene[0]),
int(Decoded_Gene[2]), 0])
elif Decoded_Gene[1] == '!':
Decoded_List.append(
[int(Decoded_Gene[0]),
int(Decoded_Gene[2]), 1])
elif Decoded_Gene[1] == '@':
Decoded_List.append(
[int(Decoded_Gene[0]),
int(Decoded_Gene[2]), 2])
elif Decoded_Gene[1] == '#':
Decoded_List.append(
[int(Decoded_Gene[0]),
int(Decoded_Gene[2]), 3])
elif Decoded_Gene[1] == '$':
Decoded_List.append(
[int(Decoded_Gene[0]),
int(Decoded_Gene[2]), 4])
else:
print("THE GENOTYPE VALUE IS UNVALID")
raise ValueError
Decoded_DNA_List.append(Decoded_List)
Decoded_RNA_List: list = Decoded_DNA_List.copy()
for decoded_dna_idx, decoded_dna in enumerate(Decoded_DNA_List):
Gene_NUM = len(decoded_dna)
for k in range(Gene_NUM):
a = Decoded_DNA_List[decoded_dna_idx][k]
for l in range(k, Gene_NUM):
b = Decoded_DNA_List[decoded_dna_idx][l]
if a and b == 1:
if decoded_dna[k][2] == 0:
Decoded_RNA_List[decoded_dna_idx].remove(
decoded_dna[l])
elif decoded_dna[k][2] == 1:
if decoded_dna[l][2] < 1:
Decoded_RNA_List[decoded_dna_idx].remove(
decoded_dna[k])
else:
Decoded_RNA_List[decoded_dna_idx].remove(
decoded_dna[l])
elif decoded_dna[k][2] == 2:
if decoded_dna[l][2] < 2:
Decoded_RNA_List[decoded_dna_idx].remove(
decoded_dna[k])
else:
Decoded_RNA_List[decoded_dna_idx].remove(
decoded_dna[l])
elif decoded_dna[k][2] == 3:
if decoded_dna[l][2] < 3:
Decoded_RNA_List[decoded_dna_idx].remove(
decoded_dna[k])
else:
Decoded_RNA_List[decoded_dna_idx].remove(
decoded_dna[l])
elif decoded_dna[k][2] == 4:
if decoded_dna[l][2] >= 4:
Decoded_RNA_List[decoded_dna_idx].remove(
decoded_dna[l])
else:
Decoded_RNA_List[decoded_dna_idx].remove(
decoded_dna[k])
else:
pass
else:
pass
for Decoded_RNA in Decoded_RNA_List:
layer_list = {}
for m in range(len(Decoded_RNA)):
for n in range(m, len(Decoded_RNA)):
if Decoded_RNA[m][1] == Decoded_RNA[n][0]:
if Decoded_RNA[n][2] == 0:
layer_list[Decoded_RNA[m][0]] = nodes.IFNodes(
n=1, traces=True)
elif Decoded_RNA[n][2] == 1:
layer_list[Decoded_RNA[m][0]] = nodes.LIFNodes(
n=1, traces=True)
elif Decoded_RNA[n][2] == 2:
layer_list[Decoded_RNA[m]
[0]] = nodes.McCullochPitts(n=1,
traces=True)
elif Decoded_RNA[n][2] == 3:
layer_list[Decoded_RNA[m]
[0]] = nodes.IzhikevichNodes(
n=1, traces=True)
elif Decoded_RNA[n][2] == 4:
layer_list[Decoded_RNA[m][0]] = nodes.SRM0Nodes(
n=1, traces=True)
else:
print("UNVALID GENO_NEURON CODE")
raise ValueError
elif n == len(Decoded_List) - 1:
layer_list[Decoded_RNA[m][1]] = nodes.LIFNodes(n=1)
for l in range(len(Decoded_RNA)):
if not Decoded_RNA[l][0] in layer_list:
if Decoded_RNA[l][2] == 0:
layer_list[Decoded_RNA[l][0]] = nodes.IFNodes(
n=1, traces=True)
elif Decoded_RNA[l][2] == 1:
layer_list[Decoded_RNA[l][0]] = nodes.LIFNodes(
n=1, traces=True)
elif Decoded_RNA[l][2] == 2:
layer_list[Decoded_RNA[l][0]] = nodes.McCullochPitts(
n=1, traces=True)
elif Decoded_RNA[l][2] == 3:
layer_list[Decoded_RNA[l][0]] = nodes.IzhikevichNodes(
n=1, traces=True)
elif Decoded_RNA[l][2] == 4:
layer_list[Decoded_RNA[l][0]] = nodes.SRM0Nodes(
n=1, traces=True)
Input_Layer = nodes.Input(n=input_N, shape=Shape, traces=True)
out = nodes.LIFNodes(n=Output_N, refrac=0, traces=True)
network.add_layer(layer=Input_Layer, name="Input Layer")
for key_l in list(layer_list.keys()):
network.add_layer(layer=layer_list[key_l], name=str(key_l))
network.add_layer(layer=out, name="Output Layer")
if len(layer_list.keys()) == 0:
layer = nodes.LIFNodes(n=1, traces=True)
network.add_layer(layer=layer, name="mid layer")
inpt_connection = Connection(source=Input_Layer,
target=layer,
w=Weight * torch.ones(input_N))
opt_connection = Connection(source=layer,
target=out,
w=Weight * torch.ones(1))
network.add_connection(inpt_connection,
source="Input_Layer",
target="mid layer")
network.add_connection(opt_connection,
source="mid layer",
target="Output Layer")
else:
for key_ic in list(layer_list.keys()):
inpt_connection = Connection(source=Input_Layer,
target=layer_list[key_ic],
w=Weight * torch.ones(input_N))
network.add_connection(inpt_connection,
source="Input_Layer",
target=str(key_ic))
for key_op in list(layer_list.keys()):
output_connection = Connection(source=layer_list[key_op],
target=out,
w=Weight * torch.ones(1),
update_rule=MSTDP)
network.add_connection(output_connection,
source=str(key_op),
target="Output Layer")
for generating_protein in Decoded_RNA:
mid_connection = Connection(
source=layer_list[generating_protein[0]],
target=layer_list[generating_protein[1]],
w=Weight * torch.ones(1),
update_rule=MSTDP)
network.add_connection(mid_connection,
source=str(generating_protein[0]),
target=str(generating_protein[1]))
network_list.append(network)
network.save('Network/' + str(i) + '.pt')
return network_list
import SNN
import NEAT
import Genetic
import ribosome
import random as rd
Generation_Num = int(input("Input Generation Number"))
gene_list = []
for i in range(Generation_Num):
gene_list = Genetic.Read_Gene()
if (len(gene_list) == 0):
import startup
gene_score_list = []
selected_gene = []
descendants_gene = []
descendant_mutated = []
score_list = []
network_list = ribosome.Translate_Into_Networks(4, [1, 4], 2, -1)
score_list = SNN.return_score(network_list, i)
gene_score_list.append(score_list)
selected_gene: list = NEAT.calc_and_select_gene(gene_score_list, gene_list)
gene_num: int = int(len(gene_list) / 2)
for k in range(gene_num):
cross1 = rd.choice(selected_gene)
cross2 = rd.choice(selected_gene)
print(cross1, cross2)
Agene1, Agene2 = NEAT.Crossover(cross1, cross2)
descendants_gene.append(Agene1)
descendants_gene.append(Agene2)
for idx_gene, gene in enumerate(descendants_gene):
descendant_mutated.append(