Example usage for edu.stanford.nlp.util PriorityQueue add

Introduction

In this page you can find the example usage for edu.stanford.nlp.util PriorityQueue add.

Prototype

public boolean add(E key, double priority);

Source Link

Document

Convenience method for if you want to pretend relaxPriority doesn't exist, or if you really want to use the return conditions of add().

Usage

From source file:conditionalCFG.ConditionalCFGParser.java

License:Open Source License

private PriorityQueue<Derivation> getCandidates(Vertex v, int k) {
    PriorityQueue<Derivation> candV = cand.get(v);
    if (candV == null) {
        candV = new BinaryHeapPriorityQueue<Derivation>();
        List<Arc> bsV = getBackwardsStar(v);

        for (Arc arc : bsV) {
            int size = arc.size();
            double score = arc.ruleScore;
            List<Double> childrenScores = new ArrayList<Double>();
            for (int i = 0; i < size; i++) {
                Vertex child = arc.tails.get(i);
                double s = iScore[child.start][child.end][child.goal];
                childrenScores.add(s);/*w w  w  .ja  va 2  s .  co m*/
                score += s;
            }
            if (score == Double.NEGATIVE_INFINITY) {
                continue;
            }
            List<Integer> j = new ArrayList<Integer>();
            for (int i = 0; i < size; i++) {
                j.add(1);
            }
            Derivation d = new Derivation(arc, j, score, childrenScores);
            candV.add(d, score);
        }
        PriorityQueue<Derivation> tmp = new BinaryHeapPriorityQueue<Derivation>();
        for (int i = 0; i < k; i++) {
            if (candV.isEmpty()) {
                break;
            }
            Derivation d = candV.removeFirst();
            tmp.add(d, d.score);
        }
        candV = tmp;
        cand.put(v, candV);
    }
    return candV;
}

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From source file:conditionalCFG.ConditionalCFGParser.java

License:Open Source License

private void lazyNext(PriorityQueue<Derivation> candV, Derivation derivation, int kPrime) {
    List<Vertex> tails = derivation.arc.tails;
    for (int i = 0, sz = derivation.arc.size(); i < sz; i++) {
        List<Integer> j = new ArrayList<Integer>(derivation.j);
        j.set(i, j.get(i) + 1);/*  w w w. j a v a2 s .  c om*/
        Vertex Ti = tails.get(i);
        lazyKthBest(Ti, j.get(i), kPrime);
        LinkedList<Derivation> dHatTi = dHat.get(Ti);
        // compute score for this derivation
        if (j.get(i) - 1 >= dHatTi.size()) {
            continue;
        }
        Derivation d = dHatTi.get(j.get(i) - 1);
        double newScore = derivation.score - derivation.childrenScores.get(i) + d.score;
        List<Double> childrenScores = new ArrayList<Double>(derivation.childrenScores);
        childrenScores.set(i, d.score);
        Derivation newDerivation = new Derivation(derivation.arc, j, newScore, childrenScores);
        if (!candV.contains(newDerivation) && newScore > Double.NEGATIVE_INFINITY) {
            candV.add(newDerivation, newScore);
        }
    }
}

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