public void evaluateInstanceList (ClassifierTrainer trainer, InstanceList instances, String description) { Classifier classifier = trainer.getClassifier(); if (classifier.getFeatureSelection() != instances.getFeatureSelection()) // TODO consider if we really want to do this... but note that the old MaxEnt did this to the testing the validation sets. //instances.setFeatureSelection(classifier.getFeatureSelection()); System.out.print (description+" accuracy=" + classifier.getAccuracy (instances)); }
public void evaluateInstanceList (ClassifierTrainer trainer, InstanceList instances, String description) { Classifier classifier = trainer.getClassifier(); if (classifier.getFeatureSelection() != instances.getFeatureSelection()) // TODO consider if we really want to do this... but note that the old MaxEnt did this to the testing the validation sets. //instances.setFeatureSelection(classifier.getFeatureSelection()); System.out.print (description+" accuracy=" + classifier.getAccuracy (instances)); }
public void evaluateInstanceList (ClassifierTrainer trainer, InstanceList instances, String description) { Classifier classifier = trainer.getClassifier(); if (classifier.getFeatureSelection() != instances.getFeatureSelection()) // TODO consider if we really want to do this... but note that the old MaxEnt did this to the testing the validation sets. //instances.setFeatureSelection(classifier.getFeatureSelection()); System.out.print (description+" accuracy=" + classifier.getAccuracy (instances)); }
/** * Test a maxent classifier. The data representation is the same as * for training. * * @param classifier the classifier to test * @param data an iterator over labeled instances * @return accuracy on the data */ static public double test(Classifier classifier, Iterator<Instance> data) { InstanceList testList = new InstanceList (classifier.getInstancePipe()); testList.addThruPipe(data); logger.info("# test instances = " + testList.size()); double accuracy = classifier.getAccuracy(testList); return accuracy; }
/** * Test a maxent classifier. The data representation is the same as * for training. * * @param classifier the classifier to test * @param data an iterator over labeled instances * @return accuracy on the data */ static public double test(Classifier classifier, Iterator<Instance> data) { InstanceList testList = new InstanceList (classifier.getInstancePipe()); testList.addThruPipe(data); logger.info("# test instances = " + testList.size()); double accuracy = classifier.getAccuracy(testList); return accuracy; }
/** * Test a maxent classifier. The data representation is the same as * for training. * * @param classifier the classifier to test * @param data an iterator over labeled instances * @return accuracy on the data */ static public double test(Classifier classifier, Iterator<Instance> data) { InstanceList testList = new InstanceList (classifier.getInstancePipe()); testList.addThruPipe(data); logger.info("# test instances = " + testList.size()); double accuracy = classifier.getAccuracy(testList); return accuracy; }
Classifier classifier = trainer.train(trainingList); logger.info("The training accuracy is "+ classifier.getAccuracy (trainingList)); features.stopGrowth(); if (save != null) {
Classifier classifier = trainer.train(trainingList); logger.info("The training accuracy is "+ classifier.getAccuracy (trainingList)); features.stopGrowth(); if (save != null) {
Classifier classifier = trainer.train(trainingList); logger.info("The training accuracy is "+ classifier.getAccuracy (trainingList)); features.stopGrowth(); if (save != null) {
logger.info("Accuracy at predicting correct/incorrect in training = " + cpc.getAccuracy(confidencePredictionTraining));
logger.info("Accuracy at predicting correct/incorrect in training = " + cpc.getAccuracy(confidencePredictionTraining));
logger.info("Accuracy at predicting correct/incorrect in training = " + cpc.getAccuracy(confidencePredictionTraining));
Classifier classifier = naiveBayesTrainer.train (ilists[0]); System.out.println ("The training accuracy is "+ classifier.getAccuracy (ilists[0])); System.out.println ("The testing accuracy is "+ classifier.getAccuracy (ilists[1]));
Classifier classifier = naiveBayesTrainer.train (ilists[0]); System.out.println ("The training accuracy is "+ classifier.getAccuracy (ilists[0])); System.out.println ("The testing accuracy is "+ classifier.getAccuracy (ilists[1]));
Classifier classifier = naiveBayesTrainer.train (ilists[0]); System.out.println ("The training accuracy is "+ classifier.getAccuracy (ilists[0])); System.out.println ("The testing accuracy is "+ classifier.getAccuracy (ilists[1]));