/**
* Computes performance measures of a tree
*
* @param inst itemsets
* @param smooth =true uses the smoothed models; otherwise uses the unsmoothed models
* @return the performance measures
* @exception Exception if something goes wrong
*/
public final Measures measures(MyDataset inst, boolean smooth) throws Exception {
int i, numItemsets, count;
double sd, y1[], y2[];
Measures measures = new Measures();
errors = this.errors(inst, smooth);
numItemsets = errors.numItemsets - errors.missingItemsets;
y1 = new double[numItemsets];
y2 = new double[numItemsets];
count = 0;
for (i = 0; i <= inst.numItemsets() - 1; i++) {
y1[count] = this.predict(inst.itemset(i), smooth);
y2[count] = inst.itemset(i).getClassValue();
count++;
}
measures.correlation = M5.correlation(y1, y2, numItemsets);
sd = M5.stdDev(inst.getClassIndex(), inst);
if (sd > 0.0) {
measures.meanAbsErr = errors.meanAbsErr;
measures.meanSqrErr = errors.meanSqrErr;
measures.type = 0;
} else {
if (numItemsets >= 1) {
measures.type = 1;
measures.meanAbsErr = errors.meanAbsErr;
measures.meanSqrErr = errors.meanSqrErr;
} else {
measures.type = 2;
measures.meanAbsErr = 0.0;
measures.meanSqrErr = 0.0;
}
}
return measures;
}
