/**
* Fisher distribution
*
* @param f Fisher value
* @param n1 N1 value
* @param n2 N2 value
* @return P-value associated
*/
private static double FishF(double f, int n1, int n2) {
double x = n2 / (n1 * f + n2);
if ((n1 % 2) == 0) {
return StatCom(1 - x, n2, n1 + n2 - 4, n2 - 2) * Math.pow(x, n2 / 2.0);
}
if ((n2 % 2) == 0) {
return 1 - StatCom(x, n1, n1 + n2 - 4, n1 - 2) * Math.pow(1 - x, n1 / 2.0);
}
double th = Math.atan(Math.sqrt(n1 * f / (1.0 * n2)));
double a = th / (Math.PI / 2.0);
double sth = Math.sin(th);
double cth = Math.cos(th);
if (n2 > 1) {
a = a + sth * cth * StatCom(cth * cth, 2, n2 - 3, -1) / (Math.PI / 2.0);
}
if (n1 == 1) {
return 1 - a;
}
double c =
4 * StatCom(sth * sth, n2 + 1, n1 + n2 - 4, n2 - 2) * sth * Math.pow(cth, n2) / Math.PI;
if (n2 == 1) {
return 1 - a + c / 2.0;
}
int k = 2;
while (k <= (n2 - 1) / 2.0) {
c = c * k / (k - .5);
k = k + 1;
}
return 1 - a + c;
}
/**
* Claculates the upper bound for the Chi-Squared value of a rule.
*
* @param suppAnte the support for the antecedent of a rule.
* @param suppCons the support for the consequent of a rule.
* @return the Chi-Squared upper bound.
*/
private double calcChiSquaredUpperBound(double suppAnte, double suppCons) {
double term;
// Test support for antecedent and confidence and choose minimum
if (suppAnte < suppCons) term = Math.pow(suppAnte - ((suppAnte * suppCons) / numRecords), 2.0);
else term = Math.pow(suppCons - ((suppAnte * suppCons) / numRecords), 2.0);
// Determine e
double eVlaue = calcWCSeValue(suppAnte, suppCons);
// Rerturn upper bound
return (term * eVlaue * numRecords);
}
/**
* It converts double to char by the gray's code
*
* @param ds the vector which is goint to change
* @param length the size of the vector
* @return the changed vector
*/
private char[] StringRep(double[] ds, int length) {
int i;
double n;
int pos;
double INCREMENTO;
char[] Cad_sal;
Cad_sal = new char[Genes * BITS_GEN + 1];
if (flag == 1) {
tmpstring = new char[Genes * BITS_GEN];
flag = 0;
}
pos = 0;
for (i = 0; i < length; i++) {
INCREMENTO = (Gene[i].max() - Gene[i].min()) / (Math.pow(2.0, (double) BITS_GEN) - 1.0);
n = (((ds[i] - Gene[i].min()) / INCREMENTO) + 0.5);
tmpstring = F.Itoc((int) n, BITS_GEN);
F.Gray(tmpstring, Cad_sal, BITS_GEN, pos);
pos += BITS_GEN;
}
return Cad_sal;
}
/**
* Calculates the Chi squared values and returns their sum.
*
* @return the sum of the Chi Squared values.
*/
private double calcChiSquaredValue() {
double sumChiSquaredValues = 0.0;
for (int index = 0; index < obsValues.length; index++) {
double chiValue = Math.pow((obsValues[index] - expValues[index]), 2.0) / expValues[index];
sumChiSquaredValues = sumChiSquaredValues + chiValue;
}
// Return
return (sumChiSquaredValues);
}
/**
* 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);
}
/**
* It Evaluates the performance of the fuzzy system. The Mean Square Error (MSE) by training is
* used
*/
public double Evaluate_fuzzy_system() {
int i;
double result, suma, fuerza;
suma = 0.0;
for (i = 0; i < train.getnData(); i++) {
fuerza = Output_fuzzy_system(train.getExample(i));
suma += Math.pow(train.getOutputAsReal(i) - fuerza, 2.0);
}
result = suma / train.getnData();
/* We want to have a maximization problem so, we invert the error */
if (result != 0.0) {
result = 1.0 / result;
}
return (result);
}
/**
* It Evaluates the performance of the best evolved fuzzy system on test data. The Mean Square
* Error (MSE) is used
*
* @return double The MSE error in test data
*/
public double Evaluate_best_fuzzy_system_in_test() {
int i;
double result, suma, fuerza;
SistemaDifuso.clear();
for (i = 0; i < Nr; i++) {
Individual indi = new Individual(BestSistemaDifuso.get(i));
SistemaDifuso.add(indi);
}
suma = 0.0;
for (i = 0; i < test.getnData(); i++) {
fuerza = Output_fuzzy_system(test.getExample(i));
suma += Math.pow(test.getOutputAsReal(i) - fuerza, 2.0);
}
result = suma / test.getnData();
return (result);
}
/**
* Obtain all exhaustive comparisons possible from an array of indexes
*
* @param indices A verctos of indexes.
* @return A vector with vectors containing all the possible relations between the indexes
*/
@SuppressWarnings("unchecked")
public static Vector<Vector<Relation>> obtainExhaustive(Vector<Integer> indices) {
Vector<Vector<Relation>> result = new Vector<Vector<Relation>>();
int i, j, k;
String binario;
boolean[] number = new boolean[indices.size()];
Vector<Integer> ind1, ind2;
Vector<Relation> set = new Vector<Relation>();
Vector<Vector<Relation>> res1, res2;
Vector<Relation> temp;
Vector<Relation> temp2;
Vector<Relation> temp3;
ind1 = new Vector<Integer>();
ind2 = new Vector<Integer>();
temp = new Vector<Relation>();
temp2 = new Vector<Relation>();
temp3 = new Vector<Relation>();
for (i = 0; i < indices.size(); i++) {
for (j = i + 1; j < indices.size(); j++) {
set.addElement(
new Relation(
((Integer) indices.elementAt(i)).intValue(),
((Integer) indices.elementAt(j)).intValue()));
}
}
if (set.size() > 0) result.addElement(set);
for (i = 1; i < (int) (Math.pow(2, indices.size() - 1)); i++) {
Arrays.fill(number, false);
ind1.removeAllElements();
ind2.removeAllElements();
temp.removeAllElements();
temp2.removeAllElements();
temp3.removeAllElements();
binario = Integer.toString(i, 2);
for (k = 0; k < number.length - binario.length(); k++) {
number[k] = false;
}
for (j = 0; j < binario.length(); j++, k++) {
if (binario.charAt(j) == '1') number[k] = true;
}
for (j = 0; j < number.length; j++) {
if (number[j] == true) {
ind1.addElement(new Integer(((Integer) indices.elementAt(j)).intValue()));
} else {
ind2.addElement(new Integer(((Integer) indices.elementAt(j)).intValue()));
}
}
res1 = obtainExhaustive(ind1);
res2 = obtainExhaustive(ind2);
for (j = 0; j < res1.size(); j++) {
result.addElement(new Vector<Relation>((Vector<Relation>) res1.elementAt(j)));
}
for (j = 0; j < res2.size(); j++) {
result.addElement(new Vector<Relation>((Vector<Relation>) res2.elementAt(j)));
}
for (j = 0; j < res1.size(); j++) {
temp = (Vector<Relation>) ((Vector<Relation>) res1.elementAt(j)).clone();
for (k = 0; k < res2.size(); k++) {
temp2 = (Vector<Relation>) temp.clone();
temp3 = (Vector<Relation>) ((Vector<Relation>) res2.elementAt(k)).clone();
if (((Relation) temp2.elementAt(0)).i < ((Relation) temp3.elementAt(0)).i) {
temp2.addAll((Vector<Relation>) temp3);
result.addElement(new Vector<Relation>(temp2));
} else {
temp3.addAll((Vector<Relation>) temp2);
result.addElement(new Vector<Relation>(temp3));
}
}
}
}
for (i = 0; i < result.size(); i++) {
if (((Vector<Relation>) result.elementAt(i)).toString().equalsIgnoreCase("[]")) {
result.removeElementAt(i);
i--;
}
}
for (i = 0; i < result.size(); i++) {
for (j = i + 1; j < result.size(); j++) {
if (((Vector<Relation>) result.elementAt(i))
.toString()
.equalsIgnoreCase(((Vector<Relation>) result.elementAt(j)).toString())) {
result.removeElementAt(j);
j--;
}
}
}
return result;
} // end-method
