def init():
drop()
print'Initializing...'
str=GeneStructure(gsNew=-1,fromDB=1,num=0)
g=Gene(structure=str,weights=[],thresholds=[],num=0)
for i in range (0,SIZE_SENSOR+SIZE_OUTPUT+1):
str.appendNeuron()
g.thresholds.append(random.uniform(-0.5,0.5))
for i in range(0,SIZE_SENSOR):
str.appendSynapse(origin=i,terminus=SIZE_SENSOR+SIZE_OUTPUT)
g.weights.append(random.uniform(-2,2))
str.appendSynapse(origin=SIZE_SENSOR+SIZE_OUTPUT,terminus=i+SIZE_SENSOR)
g.weights.append(random.uniform(-2,2))
save.saveStruct(str.pack())
g.set()
sp1=Specie(members=[g],appearTime=0)
nature=Nature(species=[sp1],generation=0,restResource=0)
save.saveCurrent(nature.packCurrent())
print'Initializing has been done.'
def breed(self):
if len(self.genes)>7:
r=[i for i in (1,WHO_MATE) if i<len(self.genes)]
for i in range(0,SPEED_MATE):
wife=choice(r)
new1=mate(gene1=self.genes[0],gene2=self.genes[wife])
self.newGenesList.append(new1)
self.genes.append(new1)
if len(self.genes)>5:
t=choice(self.genes)
new2=Gene(structure=t.structure,weights=t.weights,thresholds=t.thresholds)
new3=Gene(structure=t.structure,weights=t.weights,thresholds=t.thresholds)
new2.addNRand()
self.newSpiecesList.append(new2)
for n in new3.getSRand(sizeO=SIZE_OUTPUT,sizeS=SIZE_SENSOR):
self.newSpiecesList.append(n)
for i in range(0,SPEED_MUTATION):
tmp=choice(self.genes)
new1=Gene(structure=tmp.structure,weights=tmp.weights,thresholds=tmp.thresholds)
new1.mutation()
self.newGenesList.append(new1)
self.genes.append(new1)
def breed(self):
if len(self.genes) > 7:
r = [i for i in (1, WHO_MATE) if i < len(self.genes)]
for i in range(0, SPEED_MATE):
wife = choice(r)
new1 = mate(gene1=self.genes[0], gene2=self.genes[wife])
self.newGenesList.append(new1)
self.genes.append(new1)
if len(self.genes) > 5:
t = choice(self.genes)
new2 = Gene(structure=t.structure,
weights=t.weights,
thresholds=t.thresholds)
new3 = Gene(structure=t.structure,
weights=t.weights,
thresholds=t.thresholds)
new2.addNRand()
self.newSpiecesList.append(new2)
for n in new3.getSRand(sizeO=SIZE_OUTPUT, sizeS=SIZE_SENSOR):
self.newSpiecesList.append(n)
for i in range(0, SPEED_MUTATION):
tmp = choice(self.genes)
new1 = Gene(structure=tmp.structure,
weights=tmp.weights,
thresholds=tmp.thresholds)
new1.mutation()
self.newGenesList.append(new1)
self.genes.append(new1)
def debug(self):
print 'Infos of \'this\' unit:'
print 'Neurons:'
for i in range(0,len(self.neurons)):
print 'No.',i,':'
self.neurons[i].debug()
print 'Gene:'
self.gene.debug()
if __name__=='__main__':
from gene.gene import Gene
from gene.genestructure import GeneStructure
import random
str=GeneStructure(gsNew=-1)
g=Gene(structure=str,weights=[],thresholds=[])
for i in range (0,60):
str.appendNeuron()
g.thresholds.append(random.uniform(-0.5,0.5))
for i in range(0,20):
for j in range(20,40):
str.appendSynapse(origin=i,terminus=j)
str.appendSynapse(origin=j,terminus=i+40)
g.weights.append(random.uniform(-2,2))
g.weights.append(random.uniform(-2,2))
# str.appendSynapse(origin=0,terminus=4)
# str.appendSynapse(origin=0,terminus=5)
# str.appendSynapse(origin=1,terminus=5)
# str.appendSynapse(origin=4,terminus=3)
# str.appendSynapse(origin=4,terminus=2)
# str.appendSynapse(origin=5,terminus=2)
structure = GeneStructure(gsNew=-1,
fromDB=1,
num=num,
neurons=neurons,
sizeS=size)
members = []
for g in genes:
curGene = geneBase.find({'_id': g})[0]
weights = curGene['weights']
thresholds = curGene['thresholds']
fitness = curGene['fitness']
record = {int(k): v for k, v in dict(curGene['record']).items()}
num = curGene['_id']
gene = Gene(structure=structure,
weights=weights,
thresholds=thresholds,
fitness=fitness,
record=record,
num=num)
members.append(gene)
spicie = Specie(members=members, appearTime=appear)
spicieList.append(spicie)
nature = Nature(species=spicieList,
generation=generation,
restResource=restRes)
for i in range(0, MAX_GENERATION_PER):
nature.loop()
save.saveCurrent(nature.packCurrent())