RankedFeatureVector.Factory gainFactory = null; if (gainName.equals ("exp")) gainFactory = new ExpGain.Factory (lvs, gaussianPriorVariance); else if (gainName.equals("grad")) gainFactory = new GradientGain.Factory (lvs); gainFactory = new ExpGain.Factory (lvs, gaussianPriorVariance); else if (gainName.equals("grad")) gainFactory = new GradientGain.Factory (lvs);
gainFactory = new ExpGain.Factory (lvs, gaussianPriorVariance); else if (gainName.equals(GRADIENT_GAIN)) gainFactory = new GradientGain.Factory (lvs);
gainFactory = new ExpGain.Factory (lvs, gaussianPriorVariance); else if (gainName.equals(GRADIENT_GAIN)) gainFactory = new GradientGain.Factory (lvs);
for (int k = 0; k < s; k++) lvs[k] = (LabelVector) clusteredErrorLabelVectors[i][j].get(k); klfi[i][j] = new FeatureInducer (new ExpGain.Factory (lvs), clusteredErrorInstances[i][j], numFeaturesPerFeatureInduction); for (int i = 0; i < s; i++) lvs[i] = (LabelVector) errorLabelVectors.get(i); FeatureInducer klfi = new FeatureInducer (new ExpGain.Factory (lvs), errorInstances, numFeaturesPerFeatureInduction); klfi.induceFeaturesFor (trainingData, false, false); } else { FeatureInducer igfi = new FeatureInducer (new InfoGain.Factory(), errorInstances, numFeaturesPerFeatureInduction); igfi.induceFeaturesFor (trainingData, false, false);
for (int k = 0; k < s; k++) lvs[k] = (LabelVector) clusteredErrorLabelVectors[i][j].get(k); klfi[i][j] = new FeatureInducer (new ExpGain.Factory (lvs, gaussianPriorVariance), clusteredErrorInstances[i][j], numFeaturesPerFeatureInduction, new FeatureInducer (new ExpGain.Factory (lvs, gaussianPriorVariance), errorInstances, numFeaturesPerFeatureInduction,
RankedFeatureVector.Factory gainFactory = null; if (gainName.equals ("exp")) gainFactory = new ExpGain.Factory (lvs, gaussianPriorVariance); else if (gainName.equals("grad")) gainFactory = new GradientGain.Factory (lvs); gainFactory = new ExpGain.Factory (lvs, gaussianPriorVariance); else if (gainName.equals("grad")) gainFactory = new GradientGain.Factory (lvs);
for (int k = 0; k < s; k++) lvs[k] = (LabelVector) clusteredErrorLabelVectors[i][j].get(k); klfi[i][j] = new FeatureInducer (new ExpGain.Factory (lvs), clusteredErrorInstances[i][j], numFeaturesPerFeatureInduction); for (int i = 0; i < s; i++) lvs[i] = (LabelVector) errorLabelVectors.get(i); FeatureInducer klfi = new FeatureInducer (new ExpGain.Factory (lvs), errorInstances, numFeaturesPerFeatureInduction); logger.info("Inducing features for training set.");