func (eval singPoleEval) Evaluate(org *neat.Organism) (err error) {
if org.Phenome == nil {
err = errors.New("Cannot evaluate an org without a Phenome")
org.Fitness = []float64{0} // Minimal fitness
return
}
twelve_degrees := float64(0.2094384) // radians
num_steps := int(math.Pow(10, 5))
// initial conditions (as used by Stanley)
//rng := rand.New(rand.NewSource(time.Now().UnixNano()))
//x := float64(rng.Intn(4800))/1000.0 - 2.4
//x_dot := float64(rng.Intn(2000))/1000.0 - 1.0
//theta := float64(rng.Intn(400))/1000.0 - 0.2
//theta_dot := float64(rng.Intn(3000))/1000.0 - 1.5
var x, x_dot, theta, theta_dot float64
fitness := float64(0)
for trials := 0; trials < num_steps; trials++ {
// maps into [0,1]
inputs := []float64{(x + 2.4) / 4.8,
(x_dot + 0.75) / 1.5,
(theta + twelve_degrees) / 0.41,
(theta_dot + 1.0) / 2.0}
// a normalizacao so acontece para estas condicoes iniciais
// nada garante que a evolucao do sistema leve a outros
// valores de x, x_dot e etc...
action, err2 := org.Analyze(inputs)
if err2 != nil {
err = err2
org.Fitness = []float64{0}
return
}
// Apply action to the simulated cart-pole
x, x_dot, theta, theta_dot = cartPole(action[0], x, x_dot, theta, theta_dot)
// Check for failure. If so, return steps
// the number of steps indicates the fitness: higher = better
fitness += 1
//if fitness > 100 {
// fmt.Println("WTF?")
//}
if math.Abs(x) >= 2.4 || (math.Abs(theta) >= twelve_degrees) {
break
}
}
org.Fitness = []float64{fitness}
return
}
func (eval xorEval) Evaluate(org *neat.Organism) (err error) {
if org.Phenome == nil {
err = errors.New("Cannot evaluate an org without a Phenome")
org.Fitness = []float64{0} // Minimal fitness
return
}
e := float64(0)
for i, inputs := range INPUTS {
output, err2 := org.Analyze(inputs)
if err2 != nil {
err = err2
org.Fitness = []float64{0}
return
}
e += (output[0] - OUTPUTS[i]) * (output[0] - OUTPUTS[i])
}
org.Fitness = []float64{float64(1) - math.Sqrt(e/float64(len(OUTPUTS)))}
return
}
