Examples with LBFGS edu.neu.ccs.pyramid.optimization.LBFGS used on opensource projects

Example 1 with LBFGS

use of edu.neu.ccs.pyramid.optimization.LBFGS in project pyramid by cheng-li.

the class LogRiskOptimizer method updateModelPartial.

private void updateModelPartial() {
    if (logger.isDebugEnabled()) {
        logger.debug("start updateModelPartial()");
    }
    KLLoss klLoss = new KLLoss(crf, dataSet, targets, variance);
    // todo
    Optimizer opt = null;
    switch(optimizer) {
        case "LBFGS":
            opt = new LBFGS(klLoss);
            break;
        case "GD":
            opt = new GradientDescent(klLoss);
            break;
        default:
            throw new IllegalArgumentException("unknown");
    }
    opt.getTerminator().setMaxIteration(10);
    opt.optimize();
    if (logger.isDebugEnabled()) {
        logger.debug("finish updateModelPartial()");
    }
}

Example 2 with LBFGS

use of edu.neu.ccs.pyramid.optimization.LBFGS in project pyramid by cheng-li.

the class NoiseOptimizerLR method updateModel.

private void updateModel() {
    if (logger.isDebugEnabled()) {
        logger.debug("start updateModel()");
    }
    KLLoss klLoss = new KLLoss(crf, dataSet, targets, variance);
    // todo
    Optimizer opt = null;
    switch(optimizer) {
        case "LBFGS":
            opt = new LBFGS(klLoss);
            break;
        case "GD":
            opt = new GradientDescent(klLoss);
            break;
        default:
            throw new IllegalArgumentException("unknown");
    }
    opt.optimize();
    if (logger.isDebugEnabled()) {
        logger.debug("finish updateModel()");
    }
}

Example 3 with LBFGS

use of edu.neu.ccs.pyramid.optimization.LBFGS in project pyramid by cheng-li.

the class NoiseOptimizerLR method updateModelPartial.

private void updateModelPartial(int modelIterations) {
    if (logger.isDebugEnabled()) {
        logger.debug("start updateModelPartial()");
    }
    KLLoss klLoss = new KLLoss(crf, dataSet, targets, variance);
    // todo
    Optimizer opt = null;
    switch(optimizer) {
        case "LBFGS":
            opt = new LBFGS(klLoss);
            break;
        case "GD":
            opt = new GradientDescent(klLoss);
            break;
        default:
            throw new IllegalArgumentException("unknown");
    }
    opt.getTerminator().setMaxIteration(modelIterations);
    opt.optimize();
    if (logger.isDebugEnabled()) {
        logger.debug("finish updateModelPartial()");
    }
}

Example 4 with LBFGS

use of edu.neu.ccs.pyramid.optimization.LBFGS in project pyramid by cheng-li.

the class IMLLogisticTrainer method train.

public IMLLogisticRegression train(MultiLabelClfDataSet dataset, List<MultiLabel> assignments) {
    IMLLogisticRegression IMLLogisticRegression = new IMLLogisticRegression(dataset.getNumClasses(), dataset.getNumFeatures(), assignments);
    IMLLogisticRegression.setFeatureList(dataset.getFeatureList());
    IMLLogisticRegression.setLabelTranslator(dataset.getLabelTranslator());
    IMLLogisticLoss function = new IMLLogisticLoss(IMLLogisticRegression, dataset, gaussianPriorVariance);
    LBFGS lbfgs = new LBFGS(function);
    lbfgs.getTerminator().setRelativeEpsilon(epsilon);
    lbfgs.setHistory(history);
    lbfgs.optimize();
    return IMLLogisticRegression;
}

Example 5 with LBFGS

use of edu.neu.ccs.pyramid.optimization.LBFGS in project pyramid by cheng-li.

the class MLLogisticTrainer method train.

public MLLogisticRegression train(MultiLabelClfDataSet dataset, List<MultiLabel> assignments) {
    MLLogisticRegression mlLogisticRegression = new MLLogisticRegression(dataset.getNumClasses(), dataset.getNumFeatures(), assignments);
    mlLogisticRegression.setFeatureList(dataset.getFeatureList());
    mlLogisticRegression.setLabelTranslator(dataset.getLabelTranslator());
    mlLogisticRegression.setFeatureExtraction(false);
    MLLogisticLoss function = new MLLogisticLoss(mlLogisticRegression, dataset, gaussianPriorVariance);
    LBFGS lbfgs = new LBFGS(function);
    lbfgs.optimize();
    return mlLogisticRegression;
}