Exemple #1
0
  public MultiLinearMax(Generator featureGen, LabelAlphabet alphabet, Tree tree, int n) {
    this.featureGen = featureGen;
    this.alphabet = alphabet;
    numThread = n;
    this.tree = tree;
    pool = Executors.newFixedThreadPool(numThread);

    numClass = alphabet.size();
    if (tree == null) {
      leafs = alphabet.toSet();
    } else leafs = tree.getLeafs();
  }
Exemple #2
0
  public Results getBest(Instance inst, int n) {
    Integer target = null;
    if (isUseTarget) target = (Integer) inst.getTarget();

    SparseVector fv = featureGen.getVector(inst);

    // 每个类对应的内积
    double[] sw = new double[alphabet.size()];
    Callable<Double>[] c = new Multiplesolve[numClass];
    Future<Double>[] f = new Future[numClass];

    for (int i = 0; i < numClass; i++) {
      c[i] = new Multiplesolve(fv, i);
      f[i] = pool.submit(c[i]);
    }

    // 执行任务并获取Future对象
    for (int i = 0; i < numClass; i++) {
      try {
        sw[i] = (Double) f[i].get();
      } catch (Exception e) {
        e.printStackTrace();
        return null;
      }
    }

    Results res = new Results(n);
    if (target != null) {
      res.buildOracle();
    }

    Iterator<Integer> it = leafs.iterator();

    while (it.hasNext()) {
      double score = 0.0;
      Integer i = it.next();

      if (tree != null) { // 计算含层次信息的内积
        ArrayList<Integer> anc = tree.getPath(i);
        for (int j = 0; j < anc.size(); j++) {
          score += sw[anc.get(j)];
        }
      } else {
        score = sw[i];
      }

      // 给定目标范围是,只计算目标范围的值
      if (target != null && target.equals(i)) {
        res.addOracle(score, i);
      } else {
        res.addPred(score, i);
      }
    }
    return res;
  }