Example usage for edu.stanford.nlp.stats ClassicCounter keySet

Introduction

In this page you can find the example usage for edu.stanford.nlp.stats ClassicCounter keySet.

Prototype

@Override
public Set<E> keySet() 

Source Link

Usage

From source file:gr.aueb.cs.nlp.wordtagger.classifier.SVMWindows64Factory.java

License:Open Source License

/**
 * Converts the svm_light weight Counter (which uses feature indices) into a weight Counter
 * using the actual features and labels.  Because this is svm_light, and not svm_struct, the
 * weights for the +1 class (which correspond to labelIndex.get(0)) and the -1 class
 * (which correspond to labelIndex.get(1)) are just the negation of one another.
 *//*www.  ja  v  a  2  s .c om*/
private ClassicCounter<Pair<F, L>> convertSVMLightWeights(ClassicCounter<Integer> weights,
        Index<F> featureIndex, Index<L> labelIndex) {
    ClassicCounter<Pair<F, L>> newWeights = new ClassicCounter<Pair<F, L>>();
    for (int i : weights.keySet()) {
        F f = featureIndex.get(i - 1);
        double w = weights.getCount(i);
        // the first guy in the labelIndex was the +1 class and the second guy
        // was the -1 class
        newWeights.incrementCount(new Pair<F, L>(f, labelIndex.get(0)), w);
        newWeights.incrementCount(new Pair<F, L>(f, labelIndex.get(1)), -w);
    }
    return newWeights;
}

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From source file:gr.aueb.cs.nlp.wordtagger.classifier.SVMWindows64Factory.java

License:Open Source License

/**
 * Converts the svm_struct weight Counter (in which the weight for a feature/label pair
 * correspondes to ((labelIndex * numFeatures)+(featureIndex+1))) into a weight Counter
 * using the actual features and labels.
 */// w w  w. ja va 2s.  c  o m
private ClassicCounter<Pair<F, L>> convertSVMStructWeights(ClassicCounter<Integer> weights,
        Index<F> featureIndex, Index<L> labelIndex) {
    // int numLabels = labelIndex.size();
    int numFeatures = featureIndex.size();
    ClassicCounter<Pair<F, L>> newWeights = new ClassicCounter<Pair<F, L>>();
    for (int i : weights.keySet()) {
        L l = labelIndex.get((i - 1) / numFeatures); // integer division on purpose
        F f = featureIndex.get((i - 1) % numFeatures);
        double w = weights.getCount(i);
        newWeights.incrementCount(new Pair<F, L>(f, l), w);
    }

    return newWeights;
}

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