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individual.go
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package staticneurogenetic
import (
"math/rand"
)
type Individual struct {
Fitness float64
Genome []float64
}
func NewIndividual(genome_size int) Individual {
return Individual{
Genome: make([]float64, genome_size),
}
}
func (p *Individual) Randomize() {
for i := range p.Genome {
p.Genome[i] = rand.Float64()*2 - 1
}
}
func (p *Individual) Output(
input []float64,
layers []int,
activation ActivationFunction,
) []float64 {
var (
offset = 0
in = input
out []float64
)
for l := 1; l < len(layers); l++ {
out = make([]float64, layers[l])
for o := 0; o < layers[l]; o++ {
out[o] = p.Genome[offset] // bias
offset++
for i := 0; i < layers[l-1]; i++ {
out[o] += in[i] * p.Genome[offset]
offset++
}
out[o] = activation(out[o]) // activation
}
in = out // this output is the input for the next layer
}
return out
}
func (p *Individual) MonoParentCross(father *Individual) {
copy(p.Genome, father.Genome)
}
func (p *Individual) DivPointCross(father, mother *Individual) {
point := rand.Intn(len(p.Genome))
for i := 0; i < point; i++ {
p.Genome[i] = father.Genome[i]
}
for i := point; i < len(p.Genome); i++ {
p.Genome[i] = mother.Genome[i]
}
}
func (p *Individual) AritmeticCross(father, mother *Individual) {
alpha := rand.Float64()
for i := 0; i < len(p.Genome); i++ {
p.Genome[i] = father.Genome[i]*alpha + mother.Genome[i]*(1-alpha)
}
}
func (p *Individual) RandomCross(father, mother *Individual) {
for i := 0; i < len(p.Genome); i++ {
if rand.Float32() < 0.5 {
p.Genome[i] = father.Genome[i]
} else {
p.Genome[i] = mother.Genome[i]
}
}
}
func (p *Individual) OneMutate(mut_rate float32, mut_size float64) {
if rand.Float32() < mut_rate {
p.Genome[rand.Intn(len(p.Genome))] += rand.NormFloat64() * mut_size
}
}
func (p *Individual) MultiMutate(mut_rate float32, mut_size float64) {
for i := 0; i < len(p.Genome); i++ {
if rand.Float32() < mut_rate {
p.Genome[rand.Intn(len(p.Genome))] += rand.NormFloat64() * mut_size
}
}
}