public Clustering(List<? extends Instance> points) {
HashMap<Integer, Integer> labelMap = classValues(points);
int dim = points.get(0).dataset().numAttributes() - 1;
int numClasses = labelMap.size();
int noiseLabel;
Attribute classLabel = points.get(0).dataset().classAttribute();
int lastLabelIndex = classLabel.numValues() - 1;
if (classLabel.value(lastLabelIndex) == "noise") {
noiseLabel = lastLabelIndex;
} else {
noiseLabel = -1;
}
ArrayList<Instance>[] sorted_points = (ArrayList<Instance>[]) new ArrayList[numClasses];
for (int i = 0; i < numClasses; i++) {
sorted_points[i] = new ArrayList<Instance>();
}
for (Instance point : points) {
int clusterid = (int) point.classValue();
if (clusterid == noiseLabel) continue;
sorted_points[labelMap.get(clusterid)].add((Instance) point);
}
this.clusters = new AutoExpandVector<Cluster>();
for (int i = 0; i < numClasses; i++) {
if (sorted_points[i].size() > 0) {
SphereCluster s = new SphereCluster(sorted_points[i], dim);
s.setId(sorted_points[i].get(0).classValue());
s.setGroundTruth(sorted_points[i].get(0).classValue());
clusters.add(s);
}
}
}
