func main() {
g := GA.New()
N := len(dataPoint) / 2
// is this "metric" good in this case? could we leverage the knowledge of how classical
// more efficient fitting strategies work to choose a better fitness func?
g.CalcFitnessFunc = func(m GA.Gene) float64 {
c1 := m[0].(float64)
lam1 := m[1].(float64)
c2 := m[2].(float64)
lam2 := m[3].(float64)
s := 0.0
for i := 0; i < N; i++ {
d := fitFunc(dataPoint[2*i], c1, lam1, c2, lam2) - dataPoint[2*i+1]
s += d * d
}
return s
}
// the crossover is given by the value in the middle (is it the best idea in this case?)
g.CrossOverFunc = func(a GA.Gene, b GA.Gene) GA.Gene {
var c1, c2, lam1, lam2 [2]float64
c1[0] = a[0].(float64)
lam1[0] = a[1].(float64)
c2[0] = a[2].(float64)
lam2[0] = a[3].(float64)
c1[1] = b[0].(float64)
lam1[1] = b[1].(float64)
c2[1] = b[2].(float64)
lam2[1] = b[3].(float64)
return GA.Gene{
(c1[0] + c1[1]) / 2.0,
(lam1[0] + lam1[1]) / 2.0,
(c2[0] + c2[1]) / 2.0,
(lam2[0] + lam2[1]) / 2.0,
}
}
// mutate a "gene"; we should adjust the function to make the fit "converge" faster
g.MutationFunc = func(a GA.Gene) GA.Gene {
f := [4]float64{a[0].(float64), a[1].(float64), a[2].(float64), a[3].(float64)}
mutationProb := []float64{0.5, 0.5, 0.5, 0.5}
for i, v := range mutationProb {
p := rand.Float64()
if p < v {
f[i] += (2.0*rand.Float64() - 1.0) / 10.0
}
}
return GA.Gene{f[0], f[1], f[2], f[3]}
}
// generate initial genes; w should be chosen so that
// expected values are in (-w, w)
g.ProduceGeneFunc = func() GA.Gene {
w := 20.0 // the greater the value, the lesser we assume about the correct values
c1 := 2.0*rand.Float64() - 1.0
lam1 := 2.0*rand.Float64() - 1.0
c2 := 2.0*rand.Float64() - 1.0
lam2 := 2.0*rand.Float64() - 1.0
return GA.Gene{
c1 * w, lam1 * w, c2 * w, lam2 * w,
}
}
g.Populate(100)
g.Evolve(1500)
r := g.Winner()
// I obtain
// [2.9054365850536343 1.4071391340733141 2.9903330149276592 10.678580361408077]
// compare with 2.8891, 1.4003, 3.0068, 10.5869
// though the values are reached after fewer iterations (6, 30 ...) :-)
// conclusions: GA is not good for fitting (when compared to classical methods) ...
fmt.Fprintf(os.Stderr, "data = %v\n", r)
fmt.Println("x real fitted")
for i := 0; i < N; i++ {
fmt.Printf("%.5f %.5f %.5f\n",
dataPoint[2*i],
dataPoint[2*i+1],
fitFunc(dataPoint[2*i], r[0].(float64), r[1].(float64), r[2].(float64), r[3].(float64)))
}
}
func main() {
flag.Parse()
g := GA.New()
// the crossover is given by the value in the middle betweet a_i and b_i
g.CrossOverFunc = func(a GA.Gene, b GA.Gene) GA.Gene {
v00 := a[0].(float64)
v01 := a[1].(float64)
v02 := a[2].(float64)
v10 := b[0].(float64)
v11 := b[1].(float64)
v12 := b[2].(float64)
return GA.Gene{(v00 + v10) / 2.0, (v01 + v11) / 2.0, (v02 + v12) / 2.0}
}
// mutate a "gene"
g.MutationFunc = func(a GA.Gene) GA.Gene {
f := [3]float64{a[0].(float64), a[1].(float64), a[2].(float64)}
for i, v := range mutationProb {
p := rand.Float64()
if p < v {
f[i%len(f)] += (2.0*rand.Float64() - 1.0) / 10.0
}
}
return GA.Gene{f[0], f[1], f[2]}
}
// the generic function we use as fit function
theFunc := func(x, a, b, c float64) float64 {
return a*x*x + b*x + c
}
// this is used to generate the values; we could (should?)
// replace it with discrete data points;
// because of perturbation, let's compute points only once
fakeData := make(map[float64]float64)
for x := *minVal; x < *maxVal; x += *step {
fakeData[x] = theFunc(x, gfuncA, gfuncB, gfuncC) + *perturbation*(2.0*rand.Float64()-1.0)
}
actualFunc := func(x float64) float64 {
if val, ok := fakeData[x]; ok {
return val
} else {
if x > *maxVal || x < *minVal {
return 0.0 // outside there are no data
}
// otherwise x is between defined values: we should find them and interpolate?
// TODO ...
// for now this won't happen, so just compute a new value, if we're going to really use it
return theFunc(x, gfuncA, gfuncB, gfuncC) + *perturbation*(2.0*rand.Float64()-1.0)
}
}
// fitness given by the avg square root distance between each point
g.CalcFitnessFunc = func(m GA.Gene) float64 {
c2 := m[0].(float64)
c1 := m[1].(float64)
c0 := m[2].(float64)
s := 0.0
for x := *minVal; x < *maxVal; x += *step {
d := actualFunc(x) - theFunc(x, c2, c1, c0)
s += d * d
}
return math.Sqrt(s) / ((*maxVal - *minVal) / *step)
}
// generate initial genes; w should be chosen so that
// expected values are in (-w, w)
g.ProduceGeneFunc = func() GA.Gene {
w := *absRange // the greater the value, the lesser we assume about the correct values
a := 2.0*rand.Float64() - 1.0
b := 2.0*rand.Float64() - 1.0
c := 2.0*rand.Float64() - 1.0
return GA.Gene{
a * w, b * w, c * w,
}
}
g.Populate(*initialPop)
g.Evolve(*iterations)
res := g.Winner()
if *printWinner {
fmt.Printf("data = %v\nfitness = %v\n", res, g.CalcFitnessFunc(res))
}
if *dumpValues {
fmt.Println("x real fitted")
for x := *minVal; x < *maxVal; x += *dumpStep {
fmt.Printf("%.5f %.5f %.5f\n", x, actualFunc(x), theFunc(x, res[0].(float64), res[1].(float64), res[2].(float64)))
}
}
}
