/**
* Calculates the performance stats for the desired class and return results as a set of
* Instances.
*
* @param predictions the predictions to base the curve on
* @param classIndex index of the class of interest.
* @return datapoints as a set of instances.
*/
public Instances getCurve(FastVector predictions, int classIndex) {
if ((predictions.size() == 0)
|| (((NominalPrediction) predictions.elementAt(0)).distribution().length <= classIndex)) {
return null;
}
ThresholdCurve tc = new ThresholdCurve();
Instances threshInst = tc.getCurve(predictions, classIndex);
Instances insts = makeHeader();
int fpind = threshInst.attribute(ThresholdCurve.FP_RATE_NAME).index();
int tpind = threshInst.attribute(ThresholdCurve.TP_RATE_NAME).index();
int threshind = threshInst.attribute(ThresholdCurve.THRESHOLD_NAME).index();
double[] vals;
double fpval, tpval, thresh;
for (int i = 0; i < threshInst.numInstances(); i++) {
fpval = threshInst.instance(i).value(fpind);
tpval = threshInst.instance(i).value(tpind);
thresh = threshInst.instance(i).value(threshind);
vals = new double[3];
vals[0] = 0;
vals[1] = fpval;
vals[2] = thresh;
insts.add(new Instance(1.0, vals));
vals = new double[3];
vals[0] = 1;
vals[1] = 1.0 - tpval;
vals[2] = thresh;
insts.add(new Instance(1.0, vals));
}
return insts;
}
