public double[] getVotesForInstance(Instance inst) {
DoubleVector combinedVote = new DoubleVector();
DoubleVector confidenceVec = new DoubleVector();
int success = 0;
double[] ensembleVotes = new double[inst.numClasses()];
double[] ensMemberWeights = new double[this.ensemble.length];
boolean[] ensMemberFlags = new boolean[this.ensemble.length];
double confidence = 0.0;
for (int i = 0; i < this.ensemble.length; i++) {
if (!ensMemberFlags[i]) {
ensMemberWeights[i] = getEnsembleMemberWeight(i);
}
if (ensMemberWeights[i] > 0.0) {
DoubleVector vote = new DoubleVector(this.ensemble[i].getVotesForInstance(inst));
if (vote.sumOfValues() > 0.0) {
vote.normalize();
vote.scaleValues(ensMemberWeights[i]);
combinedVote.addValues(vote);
if (getEnsembleMemberError(i) < errorRatio) {
//
// these are the votes of the ensembles for the classes
//
success++;
// successFlag = true;
confidenceVec.addValues(vote);
ensembleVotes[confidenceVec.maxIndex()] +=
confidenceVec.getValue(confidenceVec.maxIndex());
}
}
} else {
break;
}
}
confidenceVec = (DoubleVector) combinedVote.copy();
confidenceVec.normalize();
confidence = confidenceVec.getValue(confidenceVec.maxIndex());
// Reconfigure the activeLearningRatio
// For confidence measure add to the pool and in order to fit the confidence value between 0
// and 1 divide by success val
if (confidence > confidenceThreshold) {
double qbcEntropy =
QBC.queryByCommitee(ensembleVotes, inst.numClasses(), success, ensemble.length);
Math.pow(qbcEntropy, 2);
System.out.println("QBC Entropy: " + qbcEntropy);
double activeLearningRatio = getActiveLearningRatio(qbcEntropy, combinedVote);
inst.setClassValue(combinedVote.maxIndex()); // Set the class value of the instance
instConfPool.addVotedInstance(
inst, combinedVote.getValue(combinedVote.maxIndex()), activeLearningRatio);
instConfCount++;
}
return combinedVote.getArrayRef();
}
public double getActiveLearningRatio(double qbcEntropy, DoubleVector combinedVote) {
int maxIndex = combinedVote.maxIndex();
int ensembleLength = this.ensemble.length;
double maxVote = combinedVote.getValue(maxIndex);
double activeLearningRatio = (qbcEntropy) * (maxVote / ensembleLength);
return activeLearningRatio;
}