private boolean validarRegla(Rule regla) {
int i, j;
for (i = 0; i < regla.getRule().length; i++) {
for (j = i + 1; j < regla.getRule().length; j++) {
if (regla.getiCondition(i).getAttribute() == regla.getiCondition(j).getAttribute())
return false;
}
}
return true;
}
private double computeAdjustedResidual(int data[][], int classData[], Rule regla, int clase) {
double suma = 0;
int i;
for (i = 0; i < regla.getRule().length; i++) {
suma +=
computeStandarizedResidual(data, classData, regla.getiCondition(i), clase)
/ Math.sqrt(
computeMaximumLikelohoodEstimate(data, classData, regla.getiCondition(i), clase));
}
return suma;
}
private String consecuente(
myDataset dataset, int data[][], int classData[], int infoAttr[], int nClases, Rule rule) {
int k, l;
double tmp1, tmp2;
int pos = 0, classPredicted;
double Waip;
classPredicted = -1;
Waip = 0;
tmp1 = Double.NEGATIVE_INFINITY;
for (l = 0; l < nClases; l++) {
tmp2 = 0;
for (k = 0; k < rule.getRule().length; k++) {
tmp2 += RuleSet.computeWeightEvidence(data, classData, rule.getiCondition(k), l, infoAttr);
}
if (tmp2 > tmp1) {
tmp1 = tmp2;
pos = l;
}
}
classPredicted = pos;
Waip = tmp1;
return dataset.getOutputValue(classPredicted) + " [" + Double.toString(Waip) + "]";
}
private boolean reglaPositiva(
myDataset dataset, int data[][], int classData[], int infoAttr[], int nClases, Rule rule) {
int k, l;
double tmp1, tmp2;
double Waip;
Waip = 0;
tmp1 = Double.NEGATIVE_INFINITY;
for (l = 0; l < nClases; l++) {
tmp2 = 0;
for (k = 0; k < rule.getRule().length; k++) {
tmp2 += RuleSet.computeWeightEvidence(data, classData, rule.getiCondition(k), l, infoAttr);
}
if (tmp2 > tmp1) {
tmp1 = tmp2;
}
}
Waip = tmp1;
return Waip > 0 ? true : false;
}
