當前位置: 首頁>>代碼示例>>Java>>正文

Java NaiveBayes類代碼示例

本文整理匯總了Java中weka.classifiers.bayes.NaiveBayes類的典型用法代碼示例。如果您正苦於以下問題：Java NaiveBayes類的具體用法？Java NaiveBayes怎麽用？Java NaiveBayes使用的例子？那麽, 這裏精選的類代碼示例或許可以為您提供幫助。


NaiveBayes類屬於weka.classifiers.bayes包，在下文中一共展示了NaiveBayes類的15個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Java代碼示例。

示例1: LearnNaiveBays

import weka.classifiers.bayes.NaiveBayes; //導入依賴的package包/類
@Override
public void LearnNaiveBays() throws Exception 
{
	trainedData.setClassIndex(trainedData.numAttributes()-1);
       filter=new StringToWordVector();
       classifier=new FilteredClassifier();
       classifier.setFilter(filter);
       classifier.setClassifier(new NaiveBayes());
       classifier.buildClassifier(trainedData);
}
開發者ID:unsw-cse-soc，項目名稱:Data-curation-API，代碼行數:11，代碼來源:ExtractClassificationTextNaiveBaysImpl.java

示例2: getClassifierClassName

import weka.classifiers.bayes.NaiveBayes; //導入依賴的package包/類
/**
 * Get classifier's class name by a short name
 * */
public static String getClassifierClassName(String classifierName) {
	String className = "";
	switch (classifierName) {
	case "SGD":
		className = SGD.class.toString();
		break;
	case "SGDText":
		className = SGDText.class.toString();
		break;
	case "J48":
		className = J48.class.toString();
		break;
	case "PART":
		className = PART.class.toString();
		break;
	case "NaiveBayes":
		className = NaiveBayes.class.toString();
		break;
	case "NBUpdateable":
		className = NaiveBayesUpdateable.class.toString();
		break;
	case "AdaBoostM1":
		className = AdaBoostM1.class.toString();
		break;
	case "LogitBoost":
		className = LogitBoost.class.toString();
		break;
	case "Bagging":
		className = Bagging.class.toString();
		break;
	case "Stacking":
		className = Stacking.class.toString();
		break;
	case "AdditiveRegression":
		className = AdditiveRegression.class.toString();
		break;
	case "Apriori":
		className = Apriori.class.toString();
		break;
	default:
		className = SGD.class.toString();
	}
	className = className.substring(6);
	return className;
}
開發者ID:Eyasics，項目名稱:recon，代碼行數:49，代碼來源:Util.java

示例3: generateModel

import weka.classifiers.bayes.NaiveBayes; //導入依賴的package包/類
public void generateModel(){
	nb = new NaiveBayes();
	try {
		nb.buildClassifier(iris);
	} catch (Exception e) {

	}
}
開發者ID:PacktPublishing，項目名稱:Java-Data-Science-Cookbook，代碼行數:9，代碼來源:WekaCVTest.java

示例4: testScoreWithClassifierSomeMissingFields

import weka.classifiers.bayes.NaiveBayes; //導入依賴的package包/類
@Test
public void testScoreWithClassifierSomeMissingFields() throws Exception {
  Instances train = new Instances(new BufferedReader(new StringReader(
    CorrelationMatrixMapTaskTest.IRIS)));

  train.setClassIndex(train.numAttributes() - 1);
  NaiveBayes bayes = new NaiveBayes();

  bayes.buildClassifier(train);

  WekaScoringMapTask task = new WekaScoringMapTask();
  Remove r = new Remove();
  r.setAttributeIndices("1");
  r.setInputFormat(train);
  Instances test = Filter.useFilter(train, r);

  task.setModel(bayes, train, test);

  assertTrue(task.getMissingMismatchAttributeInfo().length() > 0);
  assertTrue(task.getMissingMismatchAttributeInfo().equals(
    "sepallength missing from incoming data\n"));
  assertEquals(3, task.getPredictionLabels().size());

  for (int i = 0; i < test.numInstances(); i++) {
    assertEquals(3, task.processInstance(test.instance(i)).length);
  }
}
開發者ID:mydzigear，項目名稱:repo.kmeanspp.silhouette_score，代碼行數:28，代碼來源:WekaScoringTaskTest.java

示例5: runExps

import weka.classifiers.bayes.NaiveBayes; //導入依賴的package包/類
public void runExps(){
	Classifier c1 = new SMO();
	Classifier c2 = new J48();
	Classifier c3 = new NaiveBayes();
	trainModel(c1, "SVM");
	trainModel(c2, "J48");
	trainModel(c3, "Naive Bayes");
	
}
開發者ID:uiuc-ischool-scanr，項目名稱:SAIL，代碼行數:10，代碼來源:TrainModel.java

示例6: instance

import weka.classifiers.bayes.NaiveBayes; //導入依賴的package包/類
/**
 * Gets the appropriate subclass explainer (from the standard set that has been implemented in the toolkit),
 * depending on the classifier model (e.g. NaiveBayes, J48).
 * @param enactor
 * @param classifierWrapper
 * @return
 */
public static WekaExplainer<?> instance(Enactor enactor, ClassifierWrapper classifierWrapper) throws Exception {
	Classifier classifier = classifierWrapper.getClassifier();
	
	if (classifier instanceof NaiveBayes) {
		return new NaiveBayesExplainer(enactor, classifierWrapper);
	}
	else if (classifier instanceof J48) {
		return new J48Explainer(enactor, classifierWrapper);
	}
	else {
		return null; // TODO: doesn't support other Weka Classifiers yet
	}
}
開發者ID:claudiotrindade，項目名稱:contexttoolkit，代碼行數:21，代碼來源:WekaExplainer.java

示例7: testMultipleModels

import weka.classifiers.bayes.NaiveBayes; //導入依賴的package包/類
@Test
public void testMultipleModels() throws FOSException {
    ModelConfig modelConfig = new ModelConfig();
    modelConfig.setProperty(WekaModelConfig.CLASSIFIER_IMPL, Vote.class.getName());
    modelConfig.setProperty(WekaModelConfig.CLASSIFIER_CONFIG, "-R MAX -B \""+J48.class.getName()+"\" -B \"" + NaiveBayes.class.getName() + "\"");

    MultipleClassifiersCombiner classifier = (MultipleClassifiersCombiner)WekaClassifierFactory.create(modelConfig);
    Assert.assertEquals(2,classifier.getClassifiers().length);
    Assert.assertEquals(J48.class,classifier.getClassifiers()[0].getClass());
    Assert.assertEquals(NaiveBayes.class,classifier.getClassifiers()[1].getClass());
}
開發者ID:feedzai，項目名稱:fos-weka，代碼行數:12，代碼來源:WekaClassifierFactoryTest.java

示例8: WekaManager

import weka.classifiers.bayes.NaiveBayes; //導入依賴的package包/類
private WekaManager() {
	super();

	// set a Classifier (Naives Bayes)
	classifier = new NaiveBayes();
	classifier.setUseKernelEstimator(true);
	
	atts = new ArrayList<Attribute>();
	attsTesting = new ArrayList<Attribute>();
	attClassVals = new ArrayList<String>();
	metaData= new ArrayList<String>();
	
	mListeners = new ArrayList<IWekaListener>();
	
	isTrainingDataReceived = false;
}
開發者ID:teco-kit，項目名稱:Android-Context-Framework，代碼行數:17，代碼來源:WekaManager.java

示例9: initializeModel

import weka.classifiers.bayes.NaiveBayes; //導入依賴的package包/類
/**
 * Builds the classifier for the given training model
 */
private void initializeModel(CommonConfig config)
	throws ConfigurationException
{
	// Train the classifier
	logger.info("Training the classifier...");
	
	File arffFile = new File(modelDir + "/" + this.getClass().getSimpleName() + ".arff");
	
	classifier = new NaiveBayes();
	try {
		Instances data = DataSource.read(arffFile.getAbsolutePath());
		data.setClassIndex(data.numAttributes() - 1);
		
		classifier.buildClassifier(data);
	} catch (Exception e) {
		throw new ConfigurationException(e);
	}
}
開發者ID:hltfbk，項目名稱:Excitement-TDMLEDA，代碼行數:22，代碼來源:DKProSimilarityClassificationEDA.java

示例10: Classification

import weka.classifiers.bayes.NaiveBayes; //導入依賴的package包/類
public Classification(ArrayList<ClassifierType> cType) {

		cls = new Classifier[cType.size()];
		eval = new Evaluation[cType.size()];

		for(int i = 0; i < cType.size();i++){			
			switch(cType.get(i)){
			// TODO Will we use J48 or ID3 implementation of decision trees?
			case J48:
				cls[i] = new J48();
				break;
			case NAIVE_BAYES:
				// If bType == Incremental then cls = new UpdateableNaiveBayes(); else
				cls[i] = new NaiveBayes();
				break;
			case IBK:
				cls[i] = new IBk();
				break;
			case COSINE:
				cls[i] = useCosine();
				// TODO Add other cases: Decision Rule, KNN and so on.
			}
		}
	}
開發者ID:a-n-d-r-e-i，項目名稱:seagull，代碼行數:25，代碼來源:Classification.java

示例11: train

import weka.classifiers.bayes.NaiveBayes; //導入依賴的package包/類
public void train(TrainData data, TermVocabulary vocab) throws Exception {
	this.vocab = vocab;
	Instances trainset = buildInstances();

	for (Pair<String, BytecodeData.MethodData> p : data.data()) {

		double[] instV = seqToFeats(p.getRight().getExtOpcodeSeq());
		instV[instV.length - 1] = (double) vocab.termToPos(p.getKey());

		Instance inst = new DenseInstance(1.0, instV);
		inst.setDataset(trainset);
		trainset.add(inst);
	}

	nb = new NaiveBayes();
	nb.buildClassifier(trainset);
}
開發者ID:Keywords4Bytecodes，項目名稱:1stclass，代碼行數:18，代碼來源:NaiveBayesSystem.java

示例12: testScoreWithClassifier

import weka.classifiers.bayes.NaiveBayes; //導入依賴的package包/類
@Test
public void testScoreWithClassifier() throws Exception {
  Instances train = new Instances(new BufferedReader(new StringReader(
    CorrelationMatrixMapTaskTest.IRIS)));

  train.setClassIndex(train.numAttributes() - 1);
  NaiveBayes bayes = new NaiveBayes();

  bayes.buildClassifier(train);

  WekaScoringMapTask task = new WekaScoringMapTask();
  task.setModel(bayes, train, train);

  assertEquals(0, task.getMissingMismatchAttributeInfo().length());
  assertEquals(3, task.getPredictionLabels().size());

  for (int i = 0; i < train.numInstances(); i++) {
    assertEquals(3, task.processInstance(train.instance(i)).length);
  }
}
開發者ID:mydzigear，項目名稱:repo.kmeanspp.silhouette_score，代碼行數:21，代碼來源:WekaScoringTaskTest.java

示例13: learn

import weka.classifiers.bayes.NaiveBayes; //導入依賴的package包/類
/**
 * This method trains the classifier on the loaded dataset.
 */
public void learn() {
	try {
		trainData.setClassIndex(0);
		filter = new StringToWordVector();
		filter.setAttributeIndices("last");
		classifier = new FilteredClassifier();
		classifier.setFilter(filter);
		classifier.setClassifier(new NaiveBayes());
		
		
		classifier.buildClassifier(trainData);
		// Uncomment to see the classifier
		System.out.println("===== Training on filtered (training) dataset =====");
		//System.out.println(classifier);
	} catch (Exception e) {
		System.out.println("Problem found when training");
	}
}
開發者ID:amplia，項目名稱:weka-classifier-examples，代碼行數:22，代碼來源:Learner.java

示例14: trainClassifier

import weka.classifiers.bayes.NaiveBayes; //導入依賴的package包/類
public void trainClassifier(Classifier classifier, File trainingDataset,
                            FileOutputStream trainingModel, Integer
                                    crossValidationFoldNumber) throws Exception {

    CSVLoader csvLoader = new CSVLoader();
    csvLoader.setSource(trainingDataset);

    Instances instances = csvLoader.getDataSet();

    switch(classifier) {
        case KNN:
            int K = (int) Math.ceil(Math.sqrt(instances.numInstances()));
            this.classifier = new IBk(K);
            break;
        case NB:
            this.classifier = new NaiveBayes();
    }

    if(instances.classIndex() == -1) {
        instances.setClassIndex(instances.numAttributes() - 1);
    }

    this.classifier.buildClassifier(instances);

    if(crossValidationFoldNumber > 0) {
        Evaluation evaluation = new Evaluation(instances);
        evaluation.crossValidateModel(this.classifier, instances, crossValidationFoldNumber,
                new Random(1));
        kappa = evaluation.kappa();
        fMeasure = evaluation.weightedFMeasure();
        confusionMatrix = evaluation.toMatrixString("Confusion matrix: ");
    }

    ObjectOutputStream outputStream = new ObjectOutputStream(trainingModel);
    outputStream.writeObject(this.classifier);
    outputStream.flush();
    outputStream.close();
}
開發者ID:FlorentinTh，項目名稱:SpeakerAuthentication，代碼行數:39，代碼來源:Learning.java

示例15: main

import weka.classifiers.bayes.NaiveBayes; //導入依賴的package包/類
/**
 * @param args the command line arguments
 * @throws java.lang.Exception
 */
public static void main(String[] args) throws Exception {
    DataSource source = new DataSource("src/files/letter.arff");

    int folds = 10;
    int runs = 30;

    Classifier cls = new NaiveBayes();
    Instances data = source.getDataSet();
    data.setClassIndex(16);

    System.out.println("#seed \t correctly instances \t percentage of corrects\n");
    for (int i = 1; i <= runs; i++) {
        Evaluation eval = new Evaluation(data);
        eval.crossValidateModel(cls, data, folds, new Random(i));

        System.out.println("#" + i + "\t" + summary(eval));
    }
}
開發者ID:Unisep，項目名稱:weka-algorithms，代碼行數:23，代碼來源:WekaAlgorithms.java


注：本文中的weka.classifiers.bayes.NaiveBayes類示例由純淨天空整理自Github/MSDocs等開源代碼及文檔管理平台，相關代碼片段篩選自各路編程大神貢獻的開源項目，源碼版權歸原作者所有，傳播和使用請參考對應項目的License；未經允許，請勿轉載。