/**
* Classifies an instance w.r.t. the partitions found. It applies a naive min-distance algorithm.
*
* @param instance the instance to classify
* @return the cluster that contains the nearest point to the instance
*/
public int clusterInstance(Instance instance) throws java.lang.Exception {
DoubleMatrix1D u = DoubleFactory1D.dense.make(instance.toDoubleArray());
double min_dist = Double.POSITIVE_INFINITY;
int c = -1;
for (int i = 0; i < v.rows(); i++) {
double dist = distnorm2(u, v.viewRow(i));
if (dist < min_dist) {
c = cluster[i];
min_dist = dist;
}
}
return c;
}
public void buildClusterer(ArrayList<String> seqDB, double[][] sm) {
seqList = seqDB;
this.setSimMatrix(sm);
Attribute seqString = new Attribute("sequence", (FastVector) null);
FastVector attrInfo = new FastVector();
attrInfo.addElement(seqString);
Instances data = new Instances("data", attrInfo, 0);
for (int i = 0; i < seqList.size(); i++) {
Instance currentInst = new Instance(1);
currentInst.setDataset(data);
currentInst.setValue(0, seqList.get(i));
data.add(currentInst);
}
try {
buildClusterer(data);
} catch (Exception e) {
// TODO Auto-generated catch block
e.printStackTrace();
}
}