public BaggingClassifier train (InstanceList trainingList) { Classifier[] classifiers = new Classifier[numBags]; java.util.Random r = new java.util.Random (); for (int round = 0; round < numBags; round++) { InstanceList bag = trainingList.sampleWithReplacement (r, trainingList.size()); classifiers[round] = underlyingTrainer.newClassifierTrainer().train (bag); } this.classifier = new BaggingClassifier (trainingList.getPipe(), classifiers); return classifier; }
public Classification classify (Instance inst) { int numClasses = getLabelAlphabet().size(); double[] scores = new double[numClasses]; int bestIndex; double sum = 0; for (int i = 0; i < baggedClassifiers.length; i++) { Labeling labeling = baggedClassifiers[i].classify(inst).getLabeling(); labeling.addTo (scores); } MatrixOps.normalize (scores); return new Classification (inst, this, new LabelVector (getLabelAlphabet(), scores)); }
public Classification classify (Instance inst) { int numClasses = getLabelAlphabet().size(); double[] scores = new double[numClasses]; int bestIndex; double sum = 0; for (int i = 0; i < baggedClassifiers.length; i++) { Labeling labeling = baggedClassifiers[i].classify(inst).getLabeling(); labeling.addTo (scores); } MatrixOps.normalize (scores); return new Classification (inst, this, new LabelVector (getLabelAlphabet(), scores)); }
public BaggingClassifier train (InstanceList trainingList) { Classifier[] classifiers = new Classifier[numBags]; java.util.Random r = new java.util.Random (); for (int round = 0; round < numBags; round++) { InstanceList bag = trainingList.sampleWithReplacement (r, trainingList.size()); classifiers[round] = underlyingTrainer.newClassifierTrainer().train (bag); } this.classifier = new BaggingClassifier (trainingList.getPipe(), classifiers); return classifier; }
public Classification classify (Instance inst) { int numClasses = getLabelAlphabet().size(); double[] scores = new double[numClasses]; int bestIndex; double sum = 0; for (int i = 0; i < baggedClassifiers.length; i++) { Labeling labeling = baggedClassifiers[i].classify(inst).getLabeling(); labeling.addTo (scores); } MatrixOps.normalize (scores); return new Classification (inst, this, new LabelVector (getLabelAlphabet(), scores)); }
public BaggingClassifier train (InstanceList trainingList) { Classifier[] classifiers = new Classifier[numBags]; java.util.Random r = new java.util.Random (); for (int round = 0; round < numBags; round++) { InstanceList bag = trainingList.sampleWithReplacement (r, trainingList.size()); classifiers[round] = underlyingTrainer.newClassifierTrainer().train (bag); } this.classifier = new BaggingClassifier (trainingList.getPipe(), classifiers); return classifier; }