@Override
public ExampleSet apply(ExampleSet exampleSet) throws OperatorException {
// creating kernel and settings from Parameters
int k = Math.min(100, exampleSet.getAttributes().size() * 2);
int size = exampleSet.size();
switch (getParameterAsInt(PARAMETER_SAMPLE)) {
case SAMPLE_ABSOLUTE:
size = getParameterAsInt(PARAMETER_SAMPLE_SIZE);
break;
case SAMPLE_RELATIVE:
size = (int) Math.round(exampleSet.size() * getParameterAsDouble(PARAMETER_SAMPLE_RATIO));
break;
}
DistanceMeasure distanceMeasure = new EuclideanDistance();
distanceMeasure.init(exampleSet);
// finding farthest and nearest example to mean Vector
double[] meanVector = getMeanVector(exampleSet);
Candidate min = new Candidate(meanVector, Double.POSITIVE_INFINITY, 0);
Candidate max = new Candidate(meanVector, Double.NEGATIVE_INFINITY, 0);
int i = 0;
for (Example example : exampleSet) {
double[] exampleValues = getExampleValues(example);
Candidate current =
new Candidate(
exampleValues,
Math.abs(distanceMeasure.calculateDistance(meanVector, exampleValues)),
i);
if (current.compareTo(min) < 0) {
min = current;
}
if (current.compareTo(max) > 0) {
max = current;
}
i++;
}
ArrayList<Candidate> recentlySelected = new ArrayList<Candidate>(10);
int[] partition = new int[exampleSet.size()];
int numberOfSelectedExamples = 2;
recentlySelected.add(min);
recentlySelected.add(max);
partition[min.getExampleIndex()] = 1;
partition[max.getExampleIndex()] = 1;
double[] minimalDistances = new double[exampleSet.size()];
Arrays.fill(minimalDistances, Double.POSITIVE_INFINITY);
// running now through examples, checking for smallest distance to one of the candidates
while (numberOfSelectedExamples < size) {
TreeSet<Candidate> candidates = new TreeSet<Candidate>();
i = 0;
// check distance only for candidates recently selected.
for (Example example : exampleSet) {
// if example not has been selected allready
if (partition[i] == 0) {
double[] exampleValues = getExampleValues(example);
for (Candidate candidate : recentlySelected) {
minimalDistances[i] =
Math.min(
minimalDistances[i],
Math.abs(
distanceMeasure.calculateDistance(exampleValues, candidate.getValues())));
}
Candidate newCandidate = new Candidate(exampleValues, minimalDistances[i], i);
candidates.add(newCandidate);
if (candidates.size() > k) {
Iterator<Candidate> iterator = candidates.iterator();
iterator.next();
iterator.remove();
}
}
i++;
}
// clearing recently selected since now new ones will be selected
recentlySelected.clear();
// now running in descending order through candidates and adding to selected
// IM: descendingIterator() is not available in Java versions less than 6 !!!
// IM: Bad workaround for now by adding all candidates into a list and using a listIterator()
// and hasPrevious...
/*
Iterator<Candidate> descendingIterator = candidates.descendingIterator();
while (descendingIterator.hasNext() && numberOfSelectedExamples < desiredNumber) {
Candidate candidate = descendingIterator.next();
*/
List<Candidate> reverseCandidateList = new LinkedList<Candidate>();
Iterator<Candidate> it = candidates.iterator();
while (it.hasNext()) {
reverseCandidateList.add(it.next());
}
ListIterator<Candidate> lit =
reverseCandidateList.listIterator(reverseCandidateList.size() - 1);
while (lit.hasPrevious()) {
Candidate candidate = lit.previous();
// IM: end of workaround
boolean existSmallerDistance = false;
Iterator<Candidate> addedIterator = recentlySelected.iterator();
// test if a distance to recently selected is smaller than previously calculated minimal
// distance
// if one exists: This is not selected
while (addedIterator.hasNext()) {
double distance =
Math.abs(
distanceMeasure.calculateDistance(
addedIterator.next().getValues(), candidate.getValues()));
existSmallerDistance = existSmallerDistance || distance < candidate.getDistance();
}
if (!existSmallerDistance) {
recentlySelected.add(candidate);
partition[candidate.getExampleIndex()] = 1;
numberOfSelectedExamples++;
} else break;
}
}
// building new exampleSet containing only Examples with indices in selectedExamples
SplittedExampleSet sample = new SplittedExampleSet(exampleSet, new Partition(partition, 2));
sample.selectSingleSubset(1);
return sample;
}
public int compareTo(Candidate o) {
return Double.compare(this.distance, o.getDistance());
}