public Vector<oneClustererResult> getClusterers() {
// TODO Auto-generated method stub
double[][] nmi = new double[clustersRes.size()][clustersRes.size()];
for (int i = 0; i < clustersRes.size(); ++i) {
if (!myUtils.ClusterUtils.isAllSame(clustersRes.elementAt(i)))
for (int j = i + 1; j < clustersRes.size(); ++j) {
nmi[i][j] = ClusterUtils.NMI(clustersRes.elementAt(i), clustersRes.elementAt(j));
nmi[j][i] = nmi[i][j];
}
}
Vector<oneClustererResult> res = new Vector<oneClustererResult>();
for (int i = 0; i < clustersRes.size(); ++i) {
oneClustererResult aRes = new oneClustererResult();
aRes.cluster = clustersRes.elementAt(i).clone();
aRes.semi_weigth = this.getWeight(clustersRes.elementAt(i));
double sum_nmi = 0;
for (int j = 0; j < clustersRes.size(); ++j) {
sum_nmi += nmi[i][j];
}
// aRes.nmi_weigth = (double)1/sum_nmi; //1/beta
aRes.nmi_weigth = sum_nmi; // beta
aRes.squarederror_weigth = (double) 1 / this.SquaredError[i]; // ԽСԽºÃ
res.add(aRes);
}
NormalizedWeightAndBaseInd(res);
return res;
}
public void Evalution() {
this.ac = ClusterUtils.getAC_ByBipartiteGraph(this.classes, this.clusters);
this.nmi = ClusterUtils.NMI(this.classes, this.clusters);
}