Examples with RidgeLogisticOptimizer edu.neu.ccs.pyramid.classification.logistic_regression.RidgeLogisticOptimizer used on opensource projects

Example 11 with RidgeLogisticOptimizer

use of edu.neu.ccs.pyramid.classification.logistic_regression.RidgeLogisticOptimizer in project pyramid by cheng-li.

the class LRCBMOptimizer method updateMultiClassClassifier.

@Override
protected void updateMultiClassClassifier() {
    if (logger.isDebugEnabled()) {
        logger.debug("start updateMultiClassClassifier");
    }
    // parallel
    RidgeLogisticOptimizer ridgeLogisticOptimizer = new RidgeLogisticOptimizer((LogisticRegression) cbm.multiClassClassifier, dataSet, gammas, priorVarianceMultiClass, true);
    ridgeLogisticOptimizer.getOptimizer().getTerminator().setMaxIteration(multiclassUpdatesPerIter);
    ridgeLogisticOptimizer.optimize();
    if (logger.isDebugEnabled()) {
        logger.debug("finish updateMultiClassClassifier");
    }
}

Example 12 with RidgeLogisticOptimizer

use of edu.neu.ccs.pyramid.classification.logistic_regression.RidgeLogisticOptimizer in project pyramid by cheng-li.

the class SparseCBMOptimzer method updateBinaryLogisticRegression.

private void updateBinaryLogisticRegression(int componentIndex, int labelIndex) {
    StopWatch stopWatch = new StopWatch();
    stopWatch.start();
    double effectivePositives = effectivePositives(componentIndex, labelIndex);
    StringBuilder sb = new StringBuilder();
    sb.append("for component ").append(componentIndex).append(", label ").append(labelIndex);
    sb.append(", effective positives = ").append(effectivePositives);
    if (effectivePositives <= 1) {
        double positiveProb = prior(componentIndex, labelIndex);
        double[] probs = { 1 - positiveProb, positiveProb };
        cbm.binaryClassifiers[componentIndex][labelIndex] = new PriorProbClassifier(probs);
        sb.append(", skip, use prior = ").append(positiveProb);
        sb.append(", time spent = " + stopWatch.toString());
        System.out.println(sb.toString());
        return;
    }
    if (cbm.binaryClassifiers[componentIndex][labelIndex] == null || cbm.binaryClassifiers[componentIndex][labelIndex] instanceof PriorProbClassifier) {
        cbm.binaryClassifiers[componentIndex][labelIndex] = new LogisticRegression(2, dataSet.getNumFeatures());
    }
    RidgeLogisticOptimizer ridgeLogisticOptimizer;
    int[] binaryLabels = DataSetUtil.toBinaryLabels(dataSet.getMultiLabels(), labelIndex);
    // no parallelism
    ridgeLogisticOptimizer = new RidgeLogisticOptimizer((LogisticRegression) cbm.binaryClassifiers[componentIndex][labelIndex], dataSet, binaryLabels, activeGammas, priorVarianceBinary, false);
    //TODO maximum iterations
    ridgeLogisticOptimizer.getOptimizer().getTerminator().setMaxIteration(numBinaryUpdates);
    ridgeLogisticOptimizer.optimize();
    sb.append(", time spent = " + stopWatch.toString());
    System.out.println(sb.toString());
}