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();
}
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;
}