func (s Substrate) Decode() (neat.Network, error) {
// Create neurons from the nodes
ns := make([]network.Neuron, len(s.Nodes))
nm := make(map[int]int, len(s.Nodes))
for i, sn := range s.Nodes {
nm[sn.id] = i
ns[i] = network.Neuron{NeuronType: sn.NeuronType}
ns[i].X, ns[i].Y = sn.Position[0], sn.Position[1] // TODO: Improve this as all layers will be collapsed
switch sn.NeuronType {
case neat.Input, neat.Bias:
ns[i].ActivationType = neat.Direct
default:
ns[i].ActivationType = neat.Sigmoid
}
}
// Create synapses from the connections
cs := make([]network.Synapse, len(s.Conns))
for i, sc := range s.Conns {
cs[i] = network.Synapse{
Source: nm[sc.Source],
Target: nm[sc.Target],
Weight: sc.Weight,
}
}
// Return the new network
return network.New(ns, cs)
}
func (d Classic) decode(g neat.Genome) (net neat.Network, err error) {
// Identify the genes
nodes, conns := g.GenesByPosition()
// Create the neurons
nmap := make(map[int]int)
neurons := make([]network.Neuron, len(nodes))
for i, ng := range nodes {
nmap[ng.Innovation] = i
neurons[i] = network.Neuron{NeuronType: ng.NeuronType, ActivationType: ng.ActivationType, X: ng.X, Y: ng.Y}
}
// Create the synapses
//forward := true // Keep track of conenctions to determine if this is a feed-forward only network
synapses := make([]network.Synapse, 0, len(conns))
for _, cg := range conns {
if cg.Enabled {
//src, tgt := nodes[nmap[cg.Source]], nodes[nmap[cg.Target]]
//forward = forward && src.Y < tgt.Y
synapses = append(synapses, network.Synapse{
Source: nmap[cg.Source],
Target: nmap[cg.Target],
Weight: cg.Weight,
})
}
}
net, err = network.New(neurons, synapses)
return
}
