Examples with GoldenSectionLineSearch edu.stanford.nlp.optimization.GoldenSectionLineSearch used on opensource projects

Example 1 with GoldenSectionLineSearch

use of edu.stanford.nlp.optimization.GoldenSectionLineSearch in project CoreNLP by stanfordnlp.

the class NBLinearClassifierFactory method tuneSigma.

private void tuneSigma(final int[][] data, final int[] labels) {
    Function<Double, Double> CVSigmaToPerplexity = trialSigma -> {
        double score = 0.0;
        double sumScore = 0.0;
        int foldSize, nbCV;
        logger.info("Trying sigma = " + trialSigma);
        if (data.length >= folds) {
            foldSize = data.length / folds;
            nbCV = folds;
        } else {
            foldSize = 1;
            nbCV = data.length;
        }
        for (int j = 0; j < nbCV; j++) {
            int testMin = j * foldSize;
            int testMax = testMin + foldSize;
            LinearClassifier<L, F> c = new LinearClassifier<>(weights(data, labels, testMin, testMax, trialSigma, foldSize), featureIndex, labelIndex);
            for (int i = testMin; i < testMax; i++) {
                score -= c.logProbabilityOf(new BasicDatum<>(featureIndex.objects(data[i]))).getCount(labelIndex.get(labels[i]));
            }
            sumScore += score;
        }
        System.err.printf(": %8g%n", sumScore);
        return sumScore;
    };
    GoldenSectionLineSearch gsls = new GoldenSectionLineSearch(true);
    sigma = gsls.minimize(CVSigmaToPerplexity, 0.01, 0.0001, 2.0);
    System.out.println("Sigma used: " + sigma);
}

Example 2 with GoldenSectionLineSearch

use of edu.stanford.nlp.optimization.GoldenSectionLineSearch in project CoreNLP by stanfordnlp.

the class CRFBiasedClassifier method adjustBias.

/**
   * Adjust the bias parameter to optimize some objective function.
   * Note that this function only tunes the bias parameter of one class
   * (class of index 0), and is thus only useful for binary classification
   * problems.
   */
public void adjustBias(List<List<IN>> develData, Function<Double, Double> evalFunction, double low, double high) {
    LineSearcher ls = new GoldenSectionLineSearch(true, 1e-2, low, high);
    CRFBiasedClassifierOptimizer optimizer = new CRFBiasedClassifierOptimizer(this, evalFunction);
    double optVal = ls.minimize(optimizer);
    int bi = featureIndex.indexOf(BIAS);
    log.info("Class bias of " + weights[bi][0] + " reaches optimal value " + optVal);
}