Example usage for edu.stanford.nlp.classify LinearClassifierFactory LinearClassifierFactory

Introduction

In this page you can find the example usage for edu.stanford.nlp.classify LinearClassifierFactory LinearClassifierFactory.

Prototype

public LinearClassifierFactory() 

Source Link

Usage

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

License:Open Source License

/**
 * Constructor for more parameters./*from   www .j av  a  2s.c  o m*/
 * @param verbose, whether the classifier should log while training
 * @param maxIterations, the maximum iterations, after which the classifier should stop.
 * @param memStates, the number of previous estimate vector pairs to store
 * @param tolerance, early stopping, checking the Loss Function at every iteration,
 * and then stopping the algorithm, if it is smaller than tolerance.
 */
public MaxEntClassifier(boolean verbose, int maxIterations, int memStates, double tolerance) {
    LinearClassifierFactory<String, String> factory = new LinearClassifierFactory<>();
    factory.setTol(tolerance);
    Factory<Minimizer<DiffFunction>> minimizerCreator = customQN(verbose, maxIterations, memStates);
    factory.setMinimizerCreator(minimizerCreator);
    this.classifierFactory = factory;
}

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

License:Open Source License

/**
 * Builds a sigmoid model to turn the classifier outputs into probabilities.
 *///w ww  . j a v  a2 s.  c  om
private LinearClassifier<L, L> fitSigmoid(SVMLightClassifier<L, F> classifier, GeneralDataset<L, F> dataset) {
    RVFDataset<L, L> plattDataset = new RVFDataset<L, L>();
    for (int i = 0; i < dataset.size(); i++) {
        RVFDatum<L, F> d = dataset.getRVFDatum(i);
        Counter<L> scores = classifier.scoresOf((Datum<L, F>) d);
        scores.incrementCount(null);
        plattDataset.add(new RVFDatum<L, L>(scores, d.label()));
    }
    LinearClassifierFactory<L, L> factory = new LinearClassifierFactory<L, L>();
    factory.setPrior(new LogPrior(LogPrior.LogPriorType.NULL));
    return factory.trainClassifier(plattDataset);
}

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