Ejemplo n.º 1
0
func ExampleModel() {
	gp.SetSeed(1)
	pset := gp.CreatePrimSet(1, "x")
	pset.Add(num.Add, num.Sub, num.Mul, num.Div, num.Neg, num.V(0), num.V(1))

	problem := gp.Model{
		PrimitiveSet:  pset,
		Generator:     gp.GenFull(pset, 1, 3),
		PopSize:       500,
		Fitness:       getFitness,
		Offspring:     gp.Tournament(3),
		Mutate:        gp.MutUniform(gp.GenGrow(pset, 0, 2)),
		MutateProb:    0.2,
		Crossover:     gp.CxOnePoint(),
		CrossoverProb: 0.5,
		Threads:       1,
	}

	logger := &stats.Logger{MaxGen: 20, TargetFitness: 0.99, PrintStats: true}
	problem.Run(logger)

	// Output:
	// set random seed: 1
	// Gen      Evals    FitMax   FitAvg   FitStd   SizeAvg  SizeMax  DepthAvg DepthMax
	// 0        500      0.12     0.025    0.014    6.85     15       1.96     3
	// 1        299      0.33     0.0344   0.0204   6.33     27       1.93     6
	// 2        286      0.663    0.0469   0.0448   6.26     27       1.9      7
	// 3        265      0.663    0.0598   0.0683   6.58     34       2.06     9
	// 4        280      0.663    0.0772   0.088    7.51     39       2.39     9
	// 5        291      0.663    0.0918   0.1      8.92     32       2.82     8
	// 6        302      0.663    0.117    0.133    10.3     35       3.2      10
	// 7        294      1        0.152    0.17     11.1     35       3.48     10
	// ** SUCCESS **
}
Ejemplo n.º 2
0
func Example_gp() {
	// create initial population
	gp.SetSeed(1)
	pset := gp.CreatePrimSet(1, "x")
	pset.Add(num.Add, num.Sub, num.Mul, num.Div, num.Neg, num.V(0), num.V(1))
	generator := gp.GenFull(pset, 1, 3)
	pop, evals := gp.CreatePopulation(500, generator).Evaluate(eval{}, 1)
	best := pop.Best()
	fmt.Printf("gen=%d evals=%d fit=%.4f\n", 0, evals, best.Fitness)

	// setup genetic variations
	tournament := gp.Tournament(3)
	mutate := gp.MutUniform(gp.GenGrow(pset, 0, 2))
	crossover := gp.CxOnePoint()

	// loop till reach target fitness or exceed no. of generations
	for gen := 1; gen <= 40 && best.Fitness < 1; gen++ {
		offspring := tournament.Select(pop, len(pop))
		pop, evals = gp.VarAnd(offspring, crossover, mutate, 0.5, 0.2).Evaluate(eval{}, 1)
		best = pop.Best()
		fmt.Printf("gen=%d evals=%d fit=%.4f\n", gen, evals, best.Fitness)
	}
	fmt.Println(best.Code.Format())
	// Output:
	// set random seed: 1
	// gen=0 evals=500 fit=0.1203
	// gen=1 evals=299 fit=0.3299
	// gen=2 evals=286 fit=0.6633
	// gen=3 evals=265 fit=0.6633
	// gen=4 evals=280 fit=0.6633
	// gen=5 evals=291 fit=0.6633
	// gen=6 evals=302 fit=0.6633
	// gen=7 evals=294 fit=1.0000
	// (x + (((x / 1) - ((x / 1) * -(((x * x) + x)))) * (1 * x)))
}
Ejemplo n.º 3
0
func getStats(t *testing.T, gen int) *Stats {
	pset := gp.CreatePrimSet(1, "x")
	pset.Add(num.Add, num.Sub, num.Mul, num.Div, num.Neg, num.V(0), num.V(1))
	pop := gp.CreatePopulation(1000, gp.GenFull(pset, 1, 3))
	for i := range pop {
		pop[i].Fitness = rand.Float64()
	}
	s := Create(pop, gen, len(pop))
	t.Log(s)
	return s
}
Ejemplo n.º 4
0
// calc least squares difference and return as normalised fitness from 0->1
func (e eval) GetFitness(code gp.Expr) (float64, bool) {
	diff := 0.0
	for x := -1.0; x <= 1.0; x += 0.1 {
		val := float64(code.Eval(num.V(x)).(num.V))
		fun := x*x*x*x + x*x*x + x*x + x
		diff += (val - fun) * (val - fun)
	}
	return 1.0 / (1.0 + diff), true
}
Ejemplo n.º 5
0
// returns function to calc least squares difference and return as normalised fitness from 0->1
func fitnessFunc(trainSet []Point) func(gp.Expr) (float64, bool) {
	return func(code gp.Expr) (float64, bool) {
		diff := 0.0
		for _, pt := range trainSet {
			val := float64(code.Eval(num.V(pt.x)).(num.V))
			diff += (val - pt.y) * (val - pt.y)
		}
		return 1.0 / (1.0 + diff), true
	}
}
Ejemplo n.º 6
0
// function to plot best individual
func plotBest(trainSet []Point) func(gp.Population) stats.Plot {
	return func(pop gp.Population) stats.Plot {
		plot := stats.NewPlot("Best", len(trainSet))
		plot.Color = "#ff0000"
		code := pop.Best().Code
		for i, pt := range trainSet {
			plot.Data[i][0] = pt.x
			plot.Data[i][1] = float64(code.Eval(num.V(pt.x)).(num.V))
		}
		return plot
	}
}
Ejemplo n.º 7
0
// function to generate the random constant generator function
func ercGen(start, end int) func() num.V {
	fmt.Println("generate random constants in range", start, "to", end)
	return func() num.V {
		return num.V(start + rand.Intn(end-start+1))
	}
}