Examples with SimpleMatrix org.ejml.simple.SimpleMatrix used on opensource projects

Example 11 with SimpleMatrix

use of org.ejml.simple.SimpleMatrix in project CoreNLP by stanfordnlp.

the class SentimentPipeline method outputTreeVectors.

/**
   * Outputs the vectors from the tree.  Counts the tree nodes the
   * same as setIndexLabels.
   */
static int outputTreeVectors(PrintStream out, Tree tree, int index) {
    if (tree.isLeaf()) {
        return index;
    }
    out.print("  " + index + ":");
    SimpleMatrix vector = RNNCoreAnnotations.getNodeVector(tree);
    for (int i = 0; i < vector.getNumElements(); ++i) {
        out.print("  " + NF.format(vector.get(i)));
    }
    out.println();
    index++;
    for (Tree child : tree.children()) {
        index = outputTreeVectors(out, child, index);
    }
    return index;
}

Example 12 with SimpleMatrix

use of org.ejml.simple.SimpleMatrix in project CoreNLP by stanfordnlp.

the class SentimentPipeline method outputTreeScores.

/**
   * Outputs the scores from the tree.  Counts the tree nodes the
   * same as setIndexLabels.
   */
static int outputTreeScores(PrintStream out, Tree tree, int index) {
    if (tree.isLeaf()) {
        return index;
    }
    out.print("  " + index + ":");
    SimpleMatrix vector = RNNCoreAnnotations.getPredictions(tree);
    for (int i = 0; i < vector.getNumElements(); ++i) {
        out.print("  " + NF.format(vector.get(i)));
    }
    out.println();
    index++;
    for (Tree child : tree.children()) {
        index = outputTreeScores(out, child, index);
    }
    return index;
}

Example 13 with SimpleMatrix

use of org.ejml.simple.SimpleMatrix in project CoreNLP by stanfordnlp.

the class CategoricalFeatureExtractor method getPairFeatures.

public SimpleMatrix getPairFeatures(Pair<Integer, Integer> pair, Document document, Map<Integer, List<Mention>> mentionsByHeadIndex) {
    Mention m1 = document.predictedMentionsByID.get(pair.first);
    Mention m2 = document.predictedMentionsByID.get(pair.second);
    List<Integer> featureVals = pairwiseFeatures(document, m1, m2, dictionaries, conll);
    SimpleMatrix features = new SimpleMatrix(featureVals.size(), 1);
    for (int i = 0; i < featureVals.size(); i++) {
        features.set(i, featureVals.get(i));
    }
    features = NeuralUtils.concatenate(features, encodeDistance(m2.sentNum - m1.sentNum), encodeDistance(m2.mentionNum - m1.mentionNum - 1), new SimpleMatrix(new double[][] { { m1.sentNum == m2.sentNum && m1.endIndex > m2.startIndex ? 1 : 0 } }), getMentionFeatures(m1, document, mentionsByHeadIndex), getMentionFeatures(m2, document, mentionsByHeadIndex), encodeGenre(document));
    return features;
}

Example 14 with SimpleMatrix

use of org.ejml.simple.SimpleMatrix in project CoreNLP by stanfordnlp.

the class EmbeddingExtractor method getDocumentEmbedding.

public SimpleMatrix getDocumentEmbedding(Document document) {
    if (!conll) {
        return new SimpleMatrix(staticWordEmbeddings.getEmbeddingSize(), 1);
    }
    List<CoreLabel> words = new ArrayList<>();
    Set<Integer> seenSentences = new HashSet<>();
    for (Mention m : document.predictedMentionsByID.values()) {
        if (!seenSentences.contains(m.sentNum)) {
            seenSentences.add(m.sentNum);
            words.addAll(m.sentenceWords);
        }
    }
    return getAverageEmbedding(words);
}

Example 15 with SimpleMatrix

use of org.ejml.simple.SimpleMatrix in project CoreNLP by stanfordnlp.

the class NeuralCorefAlgorithm method runCoref.

@Override
public void runCoref(Document document) {
    List<Mention> sortedMentions = CorefUtils.getSortedMentions(document);
    Map<Integer, List<Mention>> mentionsByHeadIndex = new HashMap<>();
    for (Mention m : sortedMentions) {
        List<Mention> withIndex = mentionsByHeadIndex.get(m.headIndex);
        if (withIndex == null) {
            withIndex = new ArrayList<>();
            mentionsByHeadIndex.put(m.headIndex, withIndex);
        }
        withIndex.add(m);
    }
    SimpleMatrix documentEmbedding = embeddingExtractor.getDocumentEmbedding(document);
    Map<Integer, SimpleMatrix> antecedentEmbeddings = new HashMap<>();
    Map<Integer, SimpleMatrix> anaphorEmbeddings = new HashMap<>();
    Counter<Integer> anaphoricityScores = new ClassicCounter<>();
    for (Mention m : sortedMentions) {
        SimpleMatrix mentionEmbedding = embeddingExtractor.getMentionEmbeddings(m, documentEmbedding);
        antecedentEmbeddings.put(m.mentionID, model.getAntecedentEmbedding(mentionEmbedding));
        anaphorEmbeddings.put(m.mentionID, model.getAnaphorEmbedding(mentionEmbedding));
        anaphoricityScores.incrementCount(m.mentionID, model.getAnaphoricityScore(mentionEmbedding, featureExtractor.getAnaphoricityFeatures(m, document, mentionsByHeadIndex)));
    }
    Map<Integer, List<Integer>> mentionToCandidateAntecedents = CorefUtils.heuristicFilter(sortedMentions, maxMentionDistance, maxMentionDistanceWithStringMatch);
    for (Map.Entry<Integer, List<Integer>> e : mentionToCandidateAntecedents.entrySet()) {
        double bestScore = anaphoricityScores.getCount(e.getKey()) - 50 * (greedyness - 0.5);
        int m = e.getKey();
        Integer antecedent = null;
        for (int ca : e.getValue()) {
            double score = model.getPairwiseScore(antecedentEmbeddings.get(ca), anaphorEmbeddings.get(m), featureExtractor.getPairFeatures(new Pair<>(ca, m), document, mentionsByHeadIndex));
            if (score > bestScore) {
                bestScore = score;
                antecedent = ca;
            }
        }
        if (antecedent != null) {
            CorefUtils.mergeCoreferenceClusters(new Pair<>(antecedent, m), document);
        }
    }
}