/** * Create a NaiveBayes classifier from a set of training data. * The trainer uses counts of each feature in an instance's feature vector * to provide an estimate of p(Labeling| feature). The internal state * of the trainer is thrown away ( by a call to reset() ) when train() returns. Each * call to train() is completely independent of any other. * @param trainingList The InstanceList to be used to train the classifier. * Within each instance the data slot is an instance of FeatureVector and the * target slot is an instance of Labeling * @param validationList Currently unused * @param testSet Currently unused * @param evaluator Currently unused * @param initialClassifier Currently unused * @return The NaiveBayes classifier as trained on the trainingList */ public NaiveBayes train (InstanceList trainingList) { // Forget all the previous sufficient statistics counts; me = null; pe = null; // Train a new classifier based on this data this.classifier = trainIncremental (trainingList); return classifier; }
/** * Create a NaiveBayes classifier from a set of training data. * The trainer uses counts of each feature in an instance's feature vector * to provide an estimate of p(Labeling| feature). The internal state * of the trainer is thrown away ( by a call to reset() ) when train() returns. Each * call to train() is completely independent of any other. * @param trainingList The InstanceList to be used to train the classifier. * Within each instance the data slot is an instance of FeatureVector and the * target slot is an instance of Labeling * @param validationList Currently unused * @param testSet Currently unused * @param evaluator Currently unused * @param initialClassifier Currently unused * @return The NaiveBayes classifier as trained on the trainingList */ public NaiveBayes train (InstanceList trainingList) { // Forget all the previous sufficient statistics counts; me = null; pe = null; // Train a new classifier based on this data this.classifier = trainIncremental (trainingList); return classifier; }
/** * Create a NaiveBayes classifier from a set of training data. * The trainer uses counts of each feature in an instance's feature vector * to provide an estimate of p(Labeling| feature). The internal state * of the trainer is thrown away ( by a call to reset() ) when train() returns. Each * call to train() is completely independent of any other. * @param trainingList The InstanceList to be used to train the classifier. * Within each instance the data slot is an instance of FeatureVector and the * target slot is an instance of Labeling * @param validationList Currently unused * @param testSet Currently unused * @param evaluator Currently unused * @param initialClassifier Currently unused * @return The NaiveBayes classifier as trained on the trainingList */ public NaiveBayes train (InstanceList trainingList) { // Forget all the previous sufficient statistics counts; me = null; pe = null; // Train a new classifier based on this data this.classifier = trainIncremental (trainingList); return classifier; }
NaiveBayes classifier = trainer.trainIncremental(instList); System.out.println("target alphabet size " + instList2.getTargetAlphabet().size()); NaiveBayes classifier2 = (NaiveBayes) trainer.trainIncremental(instList2);
NaiveBayes classifier = trainer.trainIncremental(instList); System.out.println("target alphabet size " + instList2.getTargetAlphabet().size()); NaiveBayes classifier2 = (NaiveBayes) trainer.trainIncremental(instList2);
NaiveBayes classifier = (NaiveBayes) trainer.trainIncremental(instList); System.out.println("target alphabet size " + instList2.getTargetAlphabet().size()); NaiveBayes classifier2 = (NaiveBayes) trainer.trainIncremental(instList2);
NaiveBayes classifier = (NaiveBayes) trainer.trainIncremental(instList); System.out.println("target alphabet size " + instList2.getTargetAlphabet().size()); NaiveBayes classifier2 = (NaiveBayes) trainer.trainIncremental(instList2);
NaiveBayes classifier = (NaiveBayes) trainer.trainIncremental(instList); System.out.println("target alphabet size " + instList2.getTargetAlphabet().size()); NaiveBayes classifier2 = (NaiveBayes) trainer.trainIncremental(instList2); Classification secondClassification = classifier.classify("Goodbye now"); secondClassification.print();
NaiveBayes classifier = (NaiveBayes) trainer.trainIncremental(instList); System.out.println("target alphabet size " + instList2.getTargetAlphabet().size()); NaiveBayes classifier2 = (NaiveBayes) trainer.trainIncremental(instList2); Classification secondClassification = classifier.classify("Goodbye now"); secondClassification.print();