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);
 }