public Classifier train (InstanceList trainingSet) { featureSelector.selectFeaturesFor (trainingSet); // TODO What about also selecting features for the validation set? this.classifier = underlyingTrainer.train (trainingSet); return classifier; }
public Classifier train (InstanceList trainingSet) { featureSelector.selectFeaturesFor (trainingSet); // TODO What about also selecting features for the validation set? this.classifier = underlyingTrainer.train (trainingSet); return classifier; }
public Classifier train (InstanceList trainingSet) { featureSelector.selectFeaturesFor (trainingSet); // TODO What about also selecting features for the validation set? this.classifier = underlyingTrainer.train (trainingSet); return classifier; }
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 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 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 static Trial testTrainSplit(InstanceList instances) { InstanceList[] instanceLists = instances.split(new Randoms(), new double[] { 0.9, 0.1, 0.0 }); // LOG.debug("{} training instance, {} testing instances", // instanceLists[0].size(), instanceLists[1].size()); @SuppressWarnings("rawtypes") ClassifierTrainer trainer = new MaxEntTrainer(); Classifier classifier = trainer.train(instanceLists[TRAINING]); return new Trial(classifier, instanceLists[TESTING]); }
public void testRandomTrained () { ClassifierTrainer[] trainers = new ClassifierTrainer[1]; //trainers[0] = new NaiveBayesTrainer(); trainers[0] = new MaxEntTrainer(); //trainers[2] = new DecisionTreeTrainer(); Alphabet fd = dictOfSize (3); String[] classNames = new String[] {"class0", "class1", "class2"}; InstanceList ilist = new InstanceList (new Randoms(1), fd, classNames, 200); InstanceList lists[] = ilist.split (new java.util.Random(2), new double[] {.5, .5}); //System.out.println ("Training set size = "+lists[0].size()); //System.out.println ("Testing set size = "+lists[1].size()); Classifier[] classifiers = new Classifier[trainers.length]; for (int i = 0; i < trainers.length; i++) classifiers[i] = trainers[i].train (lists[0]); System.out.println ("Accuracy on training set:"); for (int i = 0; i < trainers.length; i++) System.out.println (classifiers[i].getClass().getName() + ": " + new Trial (classifiers[i], lists[0]).getAccuracy()); System.out.println ("Accuracy on testing set:"); for (int i = 0; i < trainers.length; i++) System.out.println (classifiers[i].getClass().getName() + ": " + new Trial (classifiers[i], lists[1]).getAccuracy()); }
public void testRandomTrained () { ClassifierTrainer[] trainers = new ClassifierTrainer[1]; //trainers[0] = new NaiveBayesTrainer(); trainers[0] = new MaxEntTrainer(); //trainers[2] = new DecisionTreeTrainer(); Alphabet fd = dictOfSize (3); String[] classNames = new String[] {"class0", "class1", "class2"}; InstanceList ilist = new InstanceList (new Randoms(1), fd, classNames, 200); InstanceList lists[] = ilist.split (new java.util.Random(2), new double[] {.5, .5}); //System.out.println ("Training set size = "+lists[0].size()); //System.out.println ("Testing set size = "+lists[1].size()); Classifier[] classifiers = new Classifier[trainers.length]; for (int i = 0; i < trainers.length; i++) classifiers[i] = trainers[i].train (lists[0]); System.out.println ("Accuracy on training set:"); for (int i = 0; i < trainers.length; i++) System.out.println (classifiers[i].getClass().getName() + ": " + new Trial (classifiers[i], lists[0]).getAccuracy()); System.out.println ("Accuracy on testing set:"); for (int i = 0; i < trainers.length; i++) System.out.println (classifiers[i].getClass().getName() + ": " + new Trial (classifiers[i], lists[1]).getAccuracy()); }
private double testRandomTrainedOn (InstanceList training) { ClassifierTrainer trainer = new MaxEntTrainer (); Alphabet fd = dictOfSize (3); String[] classNames = new String[] {"class0", "class1", "class2"}; Randoms r = new Randoms (1); Iterator<Instance> iter = new RandomTokenSequenceIterator (r, new Dirichlet(fd, 2.0), 30, 0, 10, 200, classNames); training.addThruPipe (iter); InstanceList testing = new InstanceList (training.getPipe ()); testing.addThruPipe (new RandomTokenSequenceIterator (r, new Dirichlet(fd, 2.0), 30, 0, 10, 200, classNames)); System.out.println ("Training set size = "+training.size()); System.out.println ("Testing set size = "+testing.size()); Classifier classifier = trainer.train (training); System.out.println ("Accuracy on training set:"); System.out.println (classifier.getClass().getName() + ": " + new Trial (classifier, training).getAccuracy()); System.out.println ("Accuracy on testing set:"); double testAcc = new Trial (classifier, testing).getAccuracy(); System.out.println (classifier.getClass().getName() + ": " + testAcc); return testAcc; }
private double testRandomTrainedOn (InstanceList training) { ClassifierTrainer trainer = new MaxEntTrainer (); Alphabet fd = dictOfSize (3); String[] classNames = new String[] {"class0", "class1", "class2"}; Randoms r = new Randoms (1); Iterator<Instance> iter = new RandomTokenSequenceIterator (r, new Dirichlet(fd, 2.0), 30, 0, 10, 200, classNames); training.addThruPipe (iter); InstanceList testing = new InstanceList (training.getPipe ()); testing.addThruPipe (new RandomTokenSequenceIterator (r, new Dirichlet(fd, 2.0), 30, 0, 10, 200, classNames)); System.out.println ("Training set size = "+training.size()); System.out.println ("Testing set size = "+testing.size()); Classifier classifier = trainer.train (training); System.out.println ("Accuracy on training set:"); System.out.println (classifier.getClass().getName() + ": " + new Trial (classifier, training).getAccuracy()); System.out.println ("Accuracy on testing set:"); double testAcc = new Trial (classifier, testing).getAccuracy(); System.out.println (classifier.getClass().getName() + ": " + testAcc); return testAcc; }
classifiers[i] = trainers[i].train (training);
classifiers[i] = trainers[i].train (training);
ClassifierTrainer<?> trainer = factory.createTrainer(); Classifier classifier = trainer.train(training);
ClassifierTrainer<?> trainer = factory.createTrainer(); Classifier classifier = trainer.train(training);
public void trainAll(ListDataSet dataSet) { Matrix dataSetInput = dataSet.getInputMatrix(); Matrix max = dataSetInput.max(Ret.NEW, Matrix.ROW); cumSum = new ArrayList<Integer>((int) max.getColumnCount()); int sum = 0; cumSum.add(sum); for (int i = (int) max.getColumnCount() - 1; i != -1; i--) { sum += max.getAsInt(0, i) + 1; cumSum.add(sum); } LabelAlphabet inputAlphabet = new LabelAlphabet(); int featureCount = getFeatureCount(dataSet); for (int i = 0; i < featureCount; i++) { // iterate from 1 to max (inclusive!) for (int fv = 1; fv < max.getAsDouble(0, i) + 1; fv++) { inputAlphabet.lookupIndex("Feature" + i + "=" + fv, true); } } LabelAlphabet targetAlphabet = new LabelAlphabet(); for (int i = 0; i < dataSet.getTargetMatrix().getColumnCount(); i++) { targetAlphabet.lookupIndex("Class" + i, true); } InstanceList trainingSet = new DataSet2InstanceList(dataSet, inputAlphabet, targetAlphabet, cumSum); classifier = trainer.train(trainingSet); }
Classifier c = underlyingClassifierTrainer.train (trainList); confusionMatrix = new ConfusionMatrix(new Trial(c, trainList));
Classifier classifier = naiveBayesTrainer.train (ilists[0]);