def crossover(self, x, y):
"""Do Gene Crossover and use real type"""
ratio = (random.random() - 0.5) * 2 * self.rat_CS
gx = Gene.Gene()
gy = Gene.Gene()
for i in range(len(x.DNA)):
gx.DNA[i] = x.DNA[i] + ratio * (x.DNA[i] - y.DNA[i])
gy.DNA[i] = y.DNA[i] - ratio * (x.DNA[i] - y.DNA[i])
return gx, gy
def addNode(self, innovationHistory):
#pick a random connection to create a node between
if len(self.genes) == 0:
self.addConnection(innovationHistory)
return
#Bias is node 0, so we don't want to lose it
if len(self.genes) > 1:
randomConnection = math.floor(
random.randint(1,
len(self.genes) - 1))
else:
randomConnection = 0
self.genes[randomConnection].enabled = False #disable it
newNodeNo = self.nextNode
self.nodes.append(Node(newNodeNo))
self.nextNode += 1
#add a new connection to the new node with a weight of 1
connectionInnovationNumber = self.getInnovationNumber(
innovationHistory, self.genes[randomConnection].fromNode,
self.getNode(newNodeNo))
self.genes.append(
Gene(self.genes[randomConnection].fromNode,
self.getNode(newNodeNo), 1, connectionInnovationNumber))
connectionInnovationNumber = self.getInnovationNumber(
innovationHistory, self.getNode(newNodeNo),
self.genes[randomConnection].toNode) #fix this
#add a new connection from the new node with a weight the same as the disabled connection
self.genes.append(
Gene(self.getNode(newNodeNo), self.genes[randomConnection].toNode,
self.genes[randomConnection].weight,
connectionInnovationNumber))
self.getNode(
newNodeNo).layer = self.genes[randomConnection].fromNode.layer + 1
connectionInnovationNumber = self.getInnovationNumber(
innovationHistory, self.nodes[self.biasNode],
self.getNode(newNodeNo))
#connect the bias to the new node with a weight of 0
self.genes.append(
Gene(self.nodes[self.biasNode], self.getNode(newNodeNo), 0,
connectionInnovationNumber))
#if the layer of the new node is equal to the layer of the output node of the old connection then a new layer needs to be created
#more accurately the layer numbers of all layers equal to or greater than this new node need to be incrimented
if self.getNode(
newNodeNo).layer == self.genes[randomConnection].toNode.layer:
for i in self.nodes: #dont include this newest node
if i.layer >= self.getNode(newNodeNo).layer:
i.layer += 1
self.layers += 1
self.connectNodes()
def crossover(self, permutation, param=Parameters):
#obtain the index list of individuals who are selected for crossover
index_pool = self.selection(permutation, param)
#offsprings list
offspring = []
#loop over the pool in order to perform the crossovers
for i in range(0, int(len(index_pool)), 2):
#sample two indices of the list to be the cut points (1, len-2)
cut_point = random.sample(range(1, param.jobs - 1), 2)
#sort the cut point
cut_point = sorted(cut_point)
#crossover then reparation, it's faster to repair than to treat before repairing
h1 = permutation[index_pool[i]].genotype[:cut_point[0]]
h2 = permutation[index_pool[i + 1]].genotype[:cut_point[0]]
t1 = permutation[index_pool[i]].genotype[cut_point[1] + 1:]
t2 = permutation[index_pool[i + 1]].genotype[cut_point[1] + 1:]
#look up the elements that are missing in both junctions of subsets
diff1 = set(list(range(0, param.jobs))) - set(h1 + t2)
diff2 = set(list(range(0, param.jobs))) - set(h2 + t1)
#initialize offsprings as new clean individuals
offspring.append(Gene.Gene(param, type=1))
offspring.append(Gene.Gene(param, type=1))
#append h1+ shuffle(diff1) + t2
offspring[i].genotype = h1 + random.sample(diff1, len(diff1)) + t2
#append h2+ shuffle(diff2) + t1
offspring[i +
1].genotype = h2 + random.sample(diff2, len(diff2)) + t1
#print(offspring[i].genotype)
#print(offspring[i+1].genotype)
#do any reparation necessary
offspring[i].genotype = list(unique_everseen(
offspring[i].genotype))
offspring[i + 1].genotype = list(
unique_everseen(offspring[i + 1].genotype))
#print(offspring[i].genotype)
#print(offspring[i+1].genotype)
#print("\n")
#print("POPULATION")
#for i in range(0, int(len(permutation))):
#print(permutation[i].genotype)
#loop over the pool in order to evaluate everything
#print("OFFSPRING")
for i in range(0, int(len(offspring))):
offspring[i].fitness = self.makespam(offspring[i].genotype, param)
#print(offspring[i].genotype, offspring[i].fitness )
return offspring
def _mutate_node(self):
gene = random.choice(list(filter(lambda g: g.enabled, self.genes)))
node = Node(self.functions,
self.nodes[gene.in_node].after + [gene.in_node])
i = len(self.nodes)
self.nodes[gene.out_node].after.append(i)
self.nodes.append(node)
self.layers[-2].append(node)
gene1 = Gene(gene.in_node, i, gene.weight,
self.new_innovation(gene.in_node, i))
gene2 = Gene(i, gene.out_node, 1,
self.new_innovation(i, gene.out_node))
self._add_gene(gene2)
self._add_gene(gene1)
gene.disable()
def __init__(self, param = Parameters):
self.population = []
self.tnPool = []
self.param = param
#list with every best element
self.bests = []
#list with all the children
self.children = []
#number of restriction
self.restrictions = param.restrictions
#define the pool size
self.pool_size = (math.floor(self.param.popNum * self.param.cRate))
#ensuring an even value
if self.pool_size % 2 != 0:
self.pool_size = self.pool_size - 1
for _ in range(0, self.param.popNum):
self.population.append(Gene.Gene(self.param.lowBound, self.param.uppBound, self.param.dim))
#as a placeholder, set the superindividual as the first element of the population
self.super = self.population[0]
def parse_sampler_params(header):
"""
Parse parameters that were used to produce a set of samples. Build a Gene instance
out of the Gene it was used on and return that as well.
"""
if header[0] == '#':
# strip header start
header = header[1:]
fields = header.split('\t')
isoforms = []
params = {}
exon_lens = {}
for field in fields:
key, value = field.split('=')
if key == 'isoforms':
isoform_desc = eval(value)
elif key == 'exon_lens':
exon_lens = dict(eval(value))
else:
params[key] = value
exons = []
for e, exon_len in exon_lens.iteritems():
exons.append(Gene.Exon(0, 0 + exon_len - 1, label=e))
gene = Gene.Gene(isoform_desc, exons)
return (params, gene)
def makeCopy(self, id):
genome = Genome(id)
genome.neurons = []
neuronMap = {}
if self.neurons is not None:
for neuron in self.neurons:
copy = Neuron(neuron.id,
neuron.type,
active=neuron.active,
trait=neuron.trait)
genome.neurons.append(copy)
neuronMap[copy.id] = copy
# XXX: not using traits
genome.genes = []
if self.genes is not None:
for gene in self.genes:
if gene.input.id in neuronMap and gene.output.id in neuronMap:
input = neuronMap[gene.input.id]
output = neuronMap[gene.output.id]
genome.genes.append(
Gene(input, output, gene.synapse.weight,
gene.synapse.recurrent, gene.synapse.trait,
gene.enabled, gene.mutation, gene.innovation))
# XXX: copying reference
genome.traits = self.traits
return genome
def addConnection(self, innovationHistory):
#cannot add a connection to a fully connected network
if self.fullyConnected():
print("connection failed")
return
#get random nodes
randomNode1 = math.floor(random.randint(1, len(self.nodes) - 1))
randomNode2 = math.floor(random.randint(1, len(self.nodes) - 1))
while self.randomConnectionNodesAreBad(randomNode1, randomNode2):
#while the random nodes are no good, get new ones
randomNode1 = math.floor(random.randint(1, len(self.nodes) - 1))
randomNode2 = math.floor(random.randint(1, len(self.nodes) - 1))
temp = 0
if self.nodes[randomNode1].layer > self.nodes[randomNode2].layer:
#if the first random node is after the second then switch
temp = randomNode2
randomNode2 = randomNode1
randomNode1 = temp
#get the innovation number of the connection
#this will be a new number if no identical genome has mutated in the same way
connectionInnovationNumber = self.getInnovationNumber(
innovationHistory, self.nodes[randomNode1],
self.nodes[randomNode2])
#add the connection with a random array
self.genes.append(
Gene(self.nodes[randomNode1], self.nodes[randomNode2],
random.uniform(-1, 1), connectionInnovationNumber))
self.connectNodes()
def reproduct(self, genelist, pickedlist):
"""Do Gene Reproduction and use real type"""
gene = Gene.Gene()
gene.f = 1e9
for i in pickedlist:
if genelist[i].f < gene.f:
gene = genelist[i].clone()
return gene
def complete_connect(
self): # call after creation to make completely connected
for node_in in self.layers[0]:
for node_out in self.layers[1]:
gene = Gene(
self.nodes.index(node_in), self.nodes.index(node_out),
random.uniform(0, 1),
self.new_innovation(self.nodes.index(node_in),
self.nodes.index(node_out)))
self._add_gene(gene)
def init(self, bestGeneList):
"""initialize"""
if not bestGeneList:
for _ in range(self.bestgenesize):
gene = Gene.Gene()
gene.f = 1e9
self.bestgenelist.append(gene)
for _ in range(self.poolsize):
gene = Gene.Gene()
gene.generate()
self.genelist.append(gene)
else:
self.bestgenelist = copy.deepcopy(bestGeneList)
percent25 = int(self.poolsize / 4)
for _ in range(percent25):
x = random.sample(range(len(self.bestgenelist)), 1)[0]
gene = self.bestgenelist[x].clone()
self.genelist.append(gene)
for _ in range(percent25, self.poolsize):
gene = Gene.Gene()
gene.generate()
self.genelist.append(gene)
def __init__(self, outname, param=Parameters):
self.population = []
self.param = param
#output file name
self.outname = outname
#list with every best element
self.bests = []
for _ in range(0, self.param.popNum):
self.population.append(Gene.Gene(self.param))
def initialize(numStrains: int = 1):
seq = rRNA.rRNA(length=5)
print(seq.seq())
gene = Gene.Gene("A", 1)
strain = Strain.Strain(name="asdasd", genome={gene.name(): gene}, ssu=seq)
community = Community.Community({strain.name(): strain})
print(community.richness())
community.speciate(strainName=strain.name(),
newStrainName="B",
changeSSU=True)
print(community.richness())
print(community.getStrain(strain.name()).ssu().seq())
return None
def __init__(self, outname, param=Parameters):
self.population = []
self.param = param
#output file name
self.outname = outname
#list with every best element
self.bests = []
#list holding information on the amount of each behavior in the population
self.behavior_ind = []
for _ in range(0, self.param.popNum):
self.population.append(Gene.Gene(self.param))
def _mutate_gene(self):
in_node = random.choice(list(sum(self.layers[:-1], [])))
connections = [
self.nodes[gene.out_node]
if self.nodes[gene.in_node] == in_node else None
for gene in self.genes
] # TODO make better
ls = filter(
lambda x: True if (self.nodes.index(x) not in in_node.after) and
(x not in connections) and (x != in_node) and
(x not in self._get_input() + [self._get_bias()]) else False,
self.nodes)
ls = list(ls)
if len(ls) == 0:
# print("no possible connections")
return None
out_node = self.nodes.index(random.choice(ls))
in_node = self.nodes.index(in_node)
weight = random.uniform(-1, 1)
innovation = self.new_innovation(in_node, out_node)
gene = Gene(in_node, out_node, weight, innovation)
self._add_gene(gene)
def main():
"""Start Gene Machine to find best parameters of RBF Network"""
ret = setinfo()
path = "./data/no_pos/"
inputt = []
outputt = []
for dirPath, fileNames in os.walk(path):
for filee in fileNames:
filee = os.path.join(dirPath, filee)
f = open(filee, "r")
for line in f.readlines():
tp = line.split()
listt = []
if isinstance(eval(tp[0]), float):
listt.append(eval(tp[0]))
listt.append(eval(tp[1]))
listt.append(eval(tp[2]))
inputt.append(listt)
outputt.append(eval(tp[3]))
else:
print tp
print "N = " + str(len(inputt))
print "input list = " + str(inputt)
print "output list = " + str(outputt)
# Normalize
for i in range(len(outputt)):
outputt[i] = (outputt[i] + 40.0) / 80.0
fError_ori = 1e9
fError_now = 1e9
bestGeneSize = int(ret.get('poolsize') / 10)
while fError_now > 5:
if os.path.isfile("./bestGA.txt"):
readfile = open("./bestGA.txt", "r")
strlist = []
for i in range(bestGeneSize):
strlist.append(readfile.readline())
readfile.close()
genelist = []
for i in range(bestGeneSize):
gene = Gene.Gene()
gene.setGene(strlist[i])
gene.calculateFitness(inputt, outputt)
genelist.append(gene)
fError_ori = genelist[0].f
print("before min function error = ", genelist[0].f)
genepool = GenePool.GenePool(ret.get("poolsize"),
ret.get('itertimes'),
ret.get("pro_CS"), ret.get("rat_CS"),
ret.get("pro_MU"), ret.get("rat_MU"),
genelist)
else:
genepool = GenePool.GenePool(ret.get("poolsize"),
ret.get('itertimes'),
ret.get("pro_CS"), ret.get("rat_CS"),
ret.get("pro_MU"), ret.get("rat_MU"),
[])
bestGeneList = genepool.geneIteration(inputt, outputt)
print("after min function error", bestGeneList[0].f)
fError_now = bestGeneList[0].f
if fError_now < fError_ori:
fw = open("./bestGA.txt", 'w')
for i in range(genepool.bestgenesize):
DNAList = bestGeneList[i].getDNAList()
s = " ".join(str(ele) for ele in DNAList) + "\n"
fw.write(s)
fw.close()
print "Training Complete!"
def get_gene(self, g):
# Get the definition file
gene_definition = get_definition_file(g)
gene = Gene.Gene(gene_definition, build=self.build, debug=self.debug)
return gene
def generate(self, size):
for i in range(0, size):
self.genes.append(gn.Gene(scdule.generate_schedule(self.events)))
return self
def mutateAddNeuron(self, population):
splitGene = None
if len(self.genes) < 15:
checking = False
for gene in self.genes:
if checking:
if randfloat(
) > 0.3 and gene.synapse.input.type != Neuron.BIAS:
splitGene = gene
break
elif gene.enabled and gene.synapse.input.type != Neuron.BIAS:
checking = True
else:
tryCount = 0
while tryCount < 20 and splitGene is None:
index = random.randint(0, len(self.genes) - 1)
if self.genes[index].enabled and self.genes[
index].synapse.input.type != Neuron.BIAS:
splitGene = self.genes[index]
break
else:
tryCount += 1
if splitGene is not None:
splitGene.enabled = False
synapse = splitGene.synapse
oldWeight = synapse.weight
input = synapse.input
output = synapse.output
newGene1 = None
newGene2 = None
newNeuron = None
found = False
for innovation in population.innovations:
if innovation.type == Innovation.NEURON and \
innovation.inId == input.id and \
innovation.outId == output.id and \
innovation.oldId == splitGene.innovation:
newNeuron = Neuron(innovation.newNeuronId)
if len(self.traits) > 0:
newNeuron.trait = self.traits[0]
newGene1 = Gene(input,
newNeuron,
1.0,
synapse.recurrent,
synapse.trait,
innovation=innovation.idA)
newGene2 = Gene(newNeuron,
output,
oldWeight,
False,
synapse.trait,
innovation=innovation.idB)
found = True
break
if not found:
id = population.getNextNeuronId()
newNeuron = Neuron(id)
if len(self.traits) > 0:
newNeuron.trait = self.traits[0]
innovationId = population.getNextInnovationId(2)
newGene1 = Gene(input,
newNeuron,
1.0,
synapse.recurrent,
synapse.trait,
innovation=innovationId)
newGene2 = Gene(newNeuron,
output,
oldWeight,
False,
synapse.trait,
innovation=innovationId + 1)
population.innovations.append(
Innovation(input.id,
output.id,
idA=innovationId,
idB=innovationId + 1,
oldId=splitGene.innovation,
type=Innovation.NEURON,
newNeuronId=newNeuron.id))
if newNeuron is not None:
self.genes.append(newGene1)
self.genes.append(newGene2)
self.neurons.append(newNeuron)
def mutateAddSynapse(self, population, tries):
recurrent = False
if randfloat() < Configuration.recurrentProbability:
recurrent = True
startIndex = 0
for neuron in self.neurons:
if neuron.type != Neuron.INPUT:
break
else:
startIndex += 1
numNeurons = len(self.neurons)
threshold = numNeurons * numNeurons
found = False
count = 0
input = None
output = None
if recurrent:
while count < tries:
inputIndex = 0
outputIndex = 0
# random recurrency
if randfloat() > 0.5:
inputIndex = random.randint(startIndex, numNeurons - 1)
outputIndex = inputIndex
else:
inputIndex = random.randint(0, numNeurons - 1)
outputIndex = random.randint(startIndex, numNeurons - 1)
input = self.neurons[inputIndex]
output = self.neurons[outputIndex]
if output.type != Neuron.INPUT and self.phenotype.isRecurrent(
input.analogue, output.analogue, 0, threshold):
for gene in self.genes:
if gene.synapse.input == input and gene.synapse.output == output and gene.synapse.recurrent:
found = True
break
if found:
break
count += 1
else:
while count < tries:
inputIndex = random.randint(0, numNeurons - 1)
outputIndex = random.randint(startIndex, numNeurons - 1)
input = self.neurons[inputIndex]
output = self.neurons[outputIndex]
if output.type != Neuron.INPUT and not self.phenotype.isRecurrent(
input.analogue, output.analogue, 0, threshold):
for gene in self.genes:
if gene.synapse.input == input and gene.synapse.output == output and not gene.synapse.recurrent:
found = True
break
if found:
break
count += 1
if found:
found = False
newGene = None
for innovation in population.innovations:
if innovation.type == Innovation.SYNAPSE and \
innovation.inId == input.id and \
innovation.outId == output.id and \
innovation.recurrent == recurrent:
newGene = Gene(input,
output,
innovation.weight,
recurrent,
self.traits[innovation.traitId],
innovation=innovation.idA)
found = True
break
if not found:
innovationId = population.getNextInnovationId()
traitIndex = random.randint(0, len(self.traits) - 1)
newWeight = randposneg() * randfloat() * 10.0
newGene = Gene(input,
output,
newWeight,
recurrent,
innovation=innovationId,
mutation=newWeight)
population.innovations.append(
Innovation(inId=input.id,
outId=output.id,
weight=newWeight,
idA=innovationId,
traitId=traitIndex,
type=Innovation.SYNAPSE,
recurrent=recurrent))
self.genes.append(newGene)
def crossover(self, type, dad, id, momFitness=0, dadFitness=0):
babyTraits = []
for i in range(len(self.traits)):
babyTraits.append(Trait(t1=self.traits[i], t2=dad.traits[i]))
babyGenes = []
babyNeurons = []
blxPos = randfloat()
momBetter = True
if momFitness < dadFitness or (momFitness == dadFitness
and len(dad.genes) < len(self.genes)):
momBetter = False
momIndex, dadIndex = 0, 0
momStopIndex, dadStopIndex = len(self.genes), len(dad.genes)
geneCounter = 0
crossoverPoint = 0
momGenes = self.genes
dadGenes = dad.genes
if type == Crossover.SINGLEPOINT:
if momStopIndex < dadStopIndex:
crossoverPoint = random.randint(0, momStopIndex)
else:
crossoverPoint = random.randint(0, dadStopIndex)
momGenes = dad.genes
momStopIndex = dadStopIndex
dadGenes = self.genes
dadStopIndex = len(dadGenes)
while momIndex < momStopIndex or dadIndex < dadStopIndex:
skip = False
chosenGene = None
disabled = False
if momIndex == momStopIndex:
chosenGene = dadGenes[dadIndex]
dadIndex += 1
if type != Crossover.SINGLEPOINT and momBetter:
skip = True
elif dadIndex == dadStopIndex:
chosenGene = momGenes[momIndex]
momIndex += 1
if type != Crossover.SINGLEPOINT and not momBetter:
skip = True
else:
momGene = momGenes[momIndex]
dadGene = dadGenes[dadIndex]
momInnovation = momGene.innovation
dadInnovation = dadGene.innovation
if momInnovation == dadInnovation:
if type == Crossover.MULTIPOINT:
if randfloat() < 0.5:
chosenGene = momGene
else:
chosenGene = dadGene
if not momGene.enabled or not dadGene.enabled:
if randfloat() < 0.75:
disabled = True
else:
useAverage = True
if type == Crossover.SINGLEPOINT:
useAverage = False
if geneCounter < crossoverPoint:
chosenGene = momGene
elif geneCounter > crossoverPoint:
chosenGene = dadGene
else:
useAverage = True
if useAverage:
enabled = True
trait = None
weight = None
input = None
output = None
recurrent = None
if randfloat() > 0.5:
trait = momGene.synapse.trait
else:
trait = dadGene.synapse.trait
if type == Crossover.BLX:
blxAlpha = -0.4
blxMax = 0.0
blxMin = 0.0
w1 = momGene.synapse.weight
w2 = dadGene.synapse.weight
if w1 > w2:
blxMax = w1
blxMin = w2
else:
blxMax = w2
blxMin = w1
blxRange = blxMax - blxMin
blxExplore = blxAlpha * blxRange
blxMin -= blxExplore
blxMax += blxExplore
blxRange = blxMax - blxMin
weight = blxMin + blxPos * blxRange
else:
weight = (momGene.synapse.weight +
dadGene.synapse.weight) / 2.0
if randfloat() > 0.5:
input = momGene.synapse.input
else:
input = dadGene.synapse.input
if randfloat() > 0.5:
output = momGene.synapse.output
else:
output = dadGene.synapse.output
if randfloat() > 0.5:
recurrent = momGene.synapse.recurrent
else:
recurrent = dadGene.synapse.recurrent
innovation = momGene.innovation
mutation = (momGene.mutation +
dadGene.mutation) / 2.0
if not momGene.enabled or not dadGene.enabled:
if randfloat() < 0.75:
enabled = False
chosenGene = Gene(input, output, weight, recurrent,
trait, enabled, mutation,
innovation)
momIndex += 1
dadIndex += 1
geneCounter += 1
elif momInnovation < dadInnovation:
if type == Crossover.SINGLEPOINT:
if geneCounter < crossoverPoint:
chosenGene = momGene
momIndex += 1
geneCounter += 1
else:
chosenGene = dadGene
dadIndex += 1
else:
chosenGene = momGene
momIndex += 1
if not momBetter:
skip = True
elif dadInnovation < momInnovation:
if type == Crossover.SINGLEPOINT:
dadIndex += 1
skip = True
else:
chosenGene = dadGene
dadIndex += 1
if momBetter:
skip = True
for gene in babyGenes:
if gene.synapse.input.id == chosenGene.synapse.input.id and \
gene.synapse.output.id == chosenGene.synapse.output.id and \
(gene.synapse.recurrent == chosenGene.synapse.recurrent or \
gene.synapse.recurrent or chosenGene.synapse.recurrent):
skip = True
break
if not skip:
traitId = 0
newInput = None
newOutput = None
if chosenGene.synapse.trait is None:
traitId = self.traits[0].id
else:
traitId = chosenGene.synapse.trait.id - self.traits[0].id
input = chosenGene.synapse.input
output = chosenGene.synapse.output
if input.id < output.id:
newInput = self.addNeuron(input, babyNeurons, babyTraits)
newOutput = self.addNeuron(output, babyNeurons, babyTraits)
else:
newOutput = self.addNeuron(output, babyNeurons, babyTraits)
newInput = self.addNeuron(input, babyNeurons, babyTraits)
newGene = Gene(newInput, newOutput, chosenGene.synapse.weight, \
chosenGene.synapse.recurrent, babyTraits[traitId], \
enabled=chosenGene.enabled, \
mutation=chosenGene.mutation, innovation=chosenGene.innovation)
if disabled:
newGene.enabled = False
disabled = False
babyGenes.append(newGene)
# TODO:
# make sure all possible input, output, and bias neurons are replicated to children
# this allows disconnected nodes to survive crossover
# see the NEAT FAQ for more info
# TODO: should also add mutateAddInput above if this is used
#for neuron in self.neurons:
# if neuron.type == Neuron.INPUT or neuron.type == Neuron.BIAS or neuron.type == Neuron.OUTPUT:
# self.addNeuron(neuron, babyNeurons, babyTraits)
# TODO: should validate the network here to make sure there is at least
# one path from at least one input to at least one output
# otherwise mutate the network until this occurs or throw the network away
return Genome(id, babyNeurons, babyGenes, babyTraits)
def load(self, file):
traitMap = {}
neuronMap = {}
for line in file.readlines():
if not line.startswith('/*'):
pieces = line.split()
type = pieces.pop(0)
if type == 'genomestart':
self.id = int(pieces[0])
elif type == 'genomeend':
if self.id != int(pieces[0]):
print "ERROR: id mismatch in genome specification"
break
elif type == 'trait':
trait = Trait(config=pieces)
self.traits.append(trait)
traitMap[trait.id] = trait
elif type == 'node':
id = int(pieces[0])
traitId = int(pieces[1])
type = int(pieces[2]) # wtf?
type = int(pieces[3])
if traitId in traitMap:
neuron = Neuron(id, type, trait=traitMap[traitId])
else:
neuron = Neuron(id, type)
self.neurons.append(neuron)
neuronMap[id] = neuron
elif type == 'gene':
traitId = int(pieces[0])
inputId = int(pieces[1])
outputId = int(pieces[2])
weight = float(pieces[3])
recurrent = int(pieces[4])
innovation = int(pieces[5])
mutation = int(pieces[6])
enabled = int(pieces[7])
if recurrent == 1:
recurrent = True
else:
recurrent = False
if enabled == 1:
enabled = True
else:
enabled = False
trait = None
input = None
output = None
if traitId in traitMap:
trait = traitMap[traitId]
if inputId in neuronMap:
input = neuronMap[inputId]
if outputId in neuronMap:
output = neuronMap[outputId]
if trait is not None and input is not None and output is not None:
self.genes.append(
Gene(input, output, weight, recurrent, trait,
enabled, mutation, innovation))
def mutation(permutation, p=Parameters):
#getting a random index
shift = random.sample(range(0, p.jobs), 1)
#saving the value of the random index
tmp = permutation[shift[0]]
#getting how many units the index will shift (it will either shift the length of permutation
#vector - 1 to the left or to the right)
move = random.randint(-(p.jobs - 2), (p.jobs - 2))
#remove the element from the list
permutation.remove(tmp)
#add the removed element in the list mod(len(list)) units to right or left
permutation.insert((shift[0] + move) % (len(permutation) + 1), tmp)
#print("VALUE:",tmp, " MOVE:", move)
return permutation
p = Parameters.Params("test")
#print(p.jobs,p.machines, p.popNum, p.tn_size, p.generations)
#print(p.joblist)
perm = []
for i in range(0, p.popNum):
perm.append(Gene.Gene(p))
#print(perm[0].genotype)
#print(mutation(perm[0].genotype,p))
for i in range(0, len(perm)):
perm[i].fitness = makespam(perm[i].genotype, p)
#selection(perm,p)
update(perm, crossover(perm, p), p)
def Kcross(lowerBound,
upperBound,
dir,
parent1=Gene,
parent2=Gene,
param=Parameters):
offspring1 = parent1.genotype
offspring2 = parent2.genotype
resultoffspring1 = list(range(param.dim))
resultoffspring2 = list(range(param.dim))
for i in range(0, param.dim):
gene1 = offspring1[i]
gene2 = offspring2[i]
if (random.uniform(0, 1) < param.getcRate()):
result = getGeneResult(gene1, gene2, param.getZeta())
if dir[i] > 0:
resultoffspring1[i] = gene1 + result
resultoffspring2[i] = gene2 + result
#bounding the problem
if resultoffspring1[i] > upperBound[i]:
resultoffspring1[1] = upperBound[i]
if resultoffspring2[i] > upperBound[i]:
resultoffspring2[1] = upperBound[i]
else:
resultoffspring1[i] = gene1 - result
resultoffspring2[i] = gene2 - result
#bounding the problem
if resultoffspring1[i] < lowerBound[i]:
resultoffspring1[1] = lowerBound[i]
if resultoffspring2[i] < lowerBound[i]:
resultoffspring2[1] = lowerBound[i]
if resultoffspring1[i] < 0:
resultoffspring1[i] = 0
if resultoffspring2[i] < 0:
resultoffspring2[i] = 0
if random.uniform(0, 1) < param.getdirCoeff():
if dir[i] > 0:
resultoffspring1[i] = gene2 - result
resultoffspring2[i] = gene1 - result
#bounding the problem
if resultoffspring1[i] < lowerBound[i]:
resultoffspring1[1] = lowerBound[i]
if resultoffspring2[i] < lowerBound[i]:
resultoffspring2[1] = lowerBound[i]
else:
resultoffspring1[i] = gene2 + result
resultoffspring2[i] = gene1 + result
#bounding the problem
if resultoffspring1[i] > upperBound[i]:
resultoffspring1[1] = upperBound[i]
if resultoffspring2[i] > upperBound[i]:
resultoffspring2[1] = upperBound[i]
else:
if dir[i] > 0:
resultoffspring1[i] = gene2 + result
resultoffspring2[i] = gene1 + result
#bounding the problem
if resultoffspring1[i] > upperBound[i]:
resultoffspring1[1] = upperBound[i]
if resultoffspring2[i] > upperBound[i]:
resultoffspring2[1] = upperBound[i]
else:
resultoffspring1[i] = gene2 - result
resultoffspring2[i] = gene1 - result
#bounding the problem
if resultoffspring1[i] < lowerBound[i]:
resultoffspring1[1] = lowerBound[i]
if resultoffspring2[i] < lowerBound[i]:
resultoffspring2[1] = lowerBound[i]
else:
resultoffspring1[i] = gene1
resultoffspring2[i] = gene2
child1 = Gene.Gene(param.lowBound, param.uppBound, param.dim)
child2 = Gene.Gene(param.lowBound, param.uppBound, param.dim)
child1.genotype = list(resultoffspring1)
child2.genotype = list(resultoffspring2)
return child1, child2