/**
* Computes the ouput of a RBF
*
* @param _input Input vector
* @return The ouput of a RBF
*/
public double evaluationRbf(double[] _input) {
double aux;
aux = RBFUtils.euclidean(_input, centre);
aux *= aux;
aux /= (2.0 * radius * radius);
return (Math.exp(-aux));
}
/**
* Chi square distribution
*
* @param x Chi^2 value
* @param n Degrees of freedom
* @return P-value associated
*/
private static double ChiSq(double x, int n) {
if (n == 1 & x > 1000) {
return 0;
}
if (x > 1000 | n > 1000) {
double q = ChiSq((x - n) * (x - n) / (2 * n), 1) / 2;
if (x > n) {
return q;
}
{
return 1 - q;
}
}
double p = Math.exp(-0.5 * x);
if ((n % 2) == 1) {
p = p * Math.sqrt(2 * x / Math.PI);
}
double k = n;
while (k >= 2) {
p = p * x / k;
k = k - 2;
}
double t = p;
double a = n;
while (t > 0.0000000001 * p) {
a = a + 2;
t = t * x / a;
p = p + t;
}
return 1 - p;
}
/**
* It calculate the matching degree between the antecedent of the rule and a given example
*
* @param indiv Individual The individual representing a fuzzy rule
* @param ejemplo double [] A given example
* @return double The matching degree between the example and the antecedent of the rule
*/
double Matching_degree(Individual indiv, double[] ejemplo) {
int i, sig;
double result, suma, numerador, denominador, sigma, ancho_intervalo;
suma = 0.0;
for (i = 0; i < entradas; i++) {
ancho_intervalo = train.getMax(i) - train.getMin(i);
sigma = -1.0;
sig = indiv.antecedente[i].sigma;
switch (sig) {
case 1:
sigma = 0.3;
break;
case 2:
sigma = 0.4;
break;
case 3:
sigma = 0.5;
break;
case 4:
sigma = 0.6;
break;
}
sigma *= ancho_intervalo;
numerador = Math.pow((ejemplo[i] - indiv.antecedente[i].m), 2.0);
denominador = Math.pow(sigma, 2.0);
suma += (numerador / denominador);
}
suma *= -1.0;
result = Math.exp(suma);
return (result);
}