@Override
protected void normalizeAttributes(Dataset Data) {
for (BaseEntry entry : Data.getEntries()) {
double[] attr = entry.getAttributes();
for (int i = 0; i < attr.length; i++) {
attr[i] = attr[i] == 0 ? 0 : Scale / attr[i];
}
entry.setAttributes(attr);
}
}
@Override
protected void normalizeClassVariable(Dataset Data) {
for (BaseEntry entry : Data.getEntries()) {
if (entry.hasMeasurement()) {
double x = entry.getMeasuredClass();
x = x == 0 ? 0.0 : Scale / x;
entry.setMeasuredClass(x);
}
if (entry.hasPrediction()) {
double x = entry.getPredictedClass();
x = x == 0 ? 0.0 : Scale / x;
entry.setPredictedClass(x);
}
}
}
@Override
protected boolean[] label(Dataset D) {
if (useMeasured && (!D.getEntry(0).hasMeasurement()))
throw new Error("Missing measured class.");
if (!useMeasured && (!D.getEntry(0).hasPrediction()))
throw new Error("Missing predicted class.");
boolean[] output = new boolean[D.NEntries()];
for (int i = 0; i < D.NEntries(); i++) {
boolean isInside = false;
double value =
useMeasured ? D.getEntry(i).getMeasuredClass() : D.getEntry(i).getPredictedClass();
if (value > lowerBound && value < upperBound) output[i] = insideRange;
else output[i] = !insideRange;
}
return output;
}
