NaiveBayes c = (NaiveBayes) nbTrainer.newClassifierTrainer().train (trainingSet); double prevLogLikelihood = 0, logLikelihood = 0; boolean converged = false; c = (NaiveBayes) nbTrainer.newClassifierTrainer().train (trainingSet2); logLikelihood = c.dataLogLikelihood (trainingSet2); System.err.println ("Loglikelihood = "+logLikelihood);
NaiveBayes c = (NaiveBayes) nbTrainer.newClassifierTrainer().train (trainingSet); double prevLogLikelihood = 0, logLikelihood = 0; boolean converged = false; c = (NaiveBayes) nbTrainer.newClassifierTrainer().train (trainingSet2); logLikelihood = c.dataLogLikelihood (trainingSet2); System.err.println ("Loglikelihood = "+logLikelihood);
NaiveBayes c = (NaiveBayes) nbTrainer.newClassifierTrainer().train (trainingSet); double prevLogLikelihood = 0, logLikelihood = 0; boolean converged = false; c = (NaiveBayes) nbTrainer.newClassifierTrainer().train (trainingSet2); logLikelihood = c.dataLogLikelihood (trainingSet2); System.err.println ("Loglikelihood = "+logLikelihood);
public void testRandomTrained () { InstanceList ilist = new InstanceList (new Randoms(1), 10, 2); Classifier c = new NaiveBayesTrainer ().train (ilist); // test on the training data int numCorrect = 0; for (int i = 0; i < ilist.size(); i++) { Instance inst = ilist.get(i); Classification cf = c.classify (inst); cf.print (); if (cf.getLabeling().getBestLabel() == inst.getLabeling().getBestLabel()) numCorrect++; } System.out.println ("Accuracy on training set = " + ((double)numCorrect)/ilist.size()); }
public void testRandomTrained () { InstanceList ilist = new InstanceList (new Randoms(1), 10, 2); Classifier c = new NaiveBayesTrainer ().train (ilist); // test on the training data int numCorrect = 0; for (int i = 0; i < ilist.size(); i++) { Instance inst = ilist.get(i); Classification cf = c.classify (inst); cf.print (); if (cf.getLabeling().getBestLabel() == inst.getLabeling().getBestLabel()) numCorrect++; } System.out.println ("Accuracy on training set = " + ((double)numCorrect)/ilist.size()); }
public void testStringTrained () { String[] africaTraining = new String[] { "on the plains of africa the lions roar", "in swahili ngoma means to dance", "nelson mandela became president of south africa", "the saraha dessert is expanding"}; String[] asiaTraining = new String[] { "panda bears eat bamboo", "china's one child policy has resulted in a surplus of boys", "tigers live in the jungle"}; InstanceList instances = new InstanceList ( new SerialPipes (new Pipe[] { new Target2Label (), new CharSequence2TokenSequence (), new TokenSequence2FeatureSequence (), new FeatureSequence2FeatureVector ()})); instances.addThruPipe (new ArrayIterator (africaTraining, "africa")); instances.addThruPipe (new ArrayIterator (asiaTraining, "asia")); Classifier c = new NaiveBayesTrainer ().train (instances); Classification cf = c.classify ("nelson mandela never eats lions"); assertTrue (cf.getLabeling().getBestLabel() == ((LabelAlphabet)instances.getTargetAlphabet()).lookupLabel("africa")); }
public void testStringTrained () { String[] africaTraining = new String[] { "on the plains of africa the lions roar", "in swahili ngoma means to dance", "nelson mandela became president of south africa", "the saraha dessert is expanding"}; String[] asiaTraining = new String[] { "panda bears eat bamboo", "china's one child policy has resulted in a surplus of boys", "tigers live in the jungle"}; InstanceList instances = new InstanceList ( new SerialPipes (new Pipe[] { new Target2Label (), new CharSequence2TokenSequence (), new TokenSequence2FeatureSequence (), new FeatureSequence2FeatureVector ()})); instances.addThruPipe (new ArrayIterator (africaTraining, "africa")); instances.addThruPipe (new ArrayIterator (asiaTraining, "asia")); Classifier c = new NaiveBayesTrainer ().train (instances); Classification cf = c.classify ("nelson mandela never eats lions"); assertTrue (cf.getLabeling().getBestLabel() == ((LabelAlphabet)instances.getTargetAlphabet()).lookupLabel("africa")); }