Classifier cModel = (Classifier)new NaiveBayes(); cModel.buildClassifier(isTrainingSet); weka.core.SerializationHelper.write("/some/where/nBayes.model", cModel); Classifier cls = (Classifier) weka.core.SerializationHelper.read("/some/where/nBayes.model"); // Test the model Evaluation eTest = new Evaluation(isTrainingSet); eTest.evaluateModel(cls, isTrainingSet);
protected void doTraining(Instances instances) throws KlabException { try { this.classifier.buildClassifier(instances); } catch (Exception e) { throw new KlabContextualizationException(e); } }
Classifier cls = null; LibLINEAR liblinear = new LibLINEAR(); liblinear.setSVMType(new SelectedTag(0, LibLINEAR.TAGS_SVMTYPE)); liblinear.setProbabilityEstimates(true); // liblinear.setBias(1); // default value cls = liblinear; cls.buildClassifier(trainingData);
public void buildClassifier() { try { if ((classifier instanceof UpdateableClassifier) == false) { Classifier auxclassifier = weka.classifiers.AbstractClassifier.makeCopy(classifier); auxclassifier.buildClassifier(instancesBuffer); classifier = auxclassifier; isBufferStoring = false; } } catch (Exception e) { System.err.println("Building WEKA Classifier: " + e.getMessage()); } }
Classifier cls = null; LibLINEAR liblinear = new LibLINEAR(); liblinear.setSVMType(new SelectedTag(0, LibLINEAR.TAGS_SVMTYPE)); liblinear.setProbabilityEstimates(true); // liblinear.setBias(1); // default value cls = liblinear; cls.buildClassifier(trainingData);
public Classifier train(Instances data, File model, List<String> parameters) throws Exception { String algoName = parameters.get(0); List<String> algoParameters = parameters.subList(1, parameters.size()); // build classifier Classifier cl = AbstractClassifier.forName(algoName, algoParameters.toArray(new String[0])); cl.buildClassifier(data); weka.core.SerializationHelper.write(model.getAbsolutePath(), cl); return cl; }
public Classifier train(Instances data, File model, List<String> parameters) throws Exception { String algoName = parameters.get(0); List<String> algoParameters = parameters.subList(1, parameters.size()); // build classifier Classifier cl = AbstractClassifier.forName(algoName, algoParameters.toArray(new String[0])); cl.buildClassifier(data); weka.core.SerializationHelper.write(model.getAbsolutePath(), cl); return cl; }
Reader r = new FileReader("/path/to/file.arff"); Instances i = new Instances(r); Classifier c = new J48(); c.buildClassifier(i);
public Evaluation classify(Classifier model, Instances trainingSet, Instances testingSet) throws Exception { Evaluation evaluation = new Evaluation(trainingSet); model.buildClassifier(trainingSet); evaluation.evaluateModel(model, testingSet); return evaluation; }
@Override public void buildClassifier(Instances D) throws Exception { testCapabilities(D); int L = D.classIndex(); if(getDebug()) System.out.print("transforming labels with size: "+L+" baseModel: "+m_Classifier.getClass().getName()+" "); Instances transformed_D = this.transformLabels(D); m_Classifier.buildClassifier(transformed_D); }
@Override public void buildClassifier(Instances D) throws Exception { testCapabilities(D); int L = D.classIndex(); if(getDebug()) System.out.print("transforming labels with size: "+L+" baseModel: "+m_Classifier.getClass().getName()+" "); Instances transformed_D = this.transformLabels(D); m_Classifier.buildClassifier(transformed_D); }
1. filteredData = new Instances(new BufferedReader(new FileReader("/Users/Passionate/Desktop/train_std.arff"))); 2. Instances filteredTests= new Instances(new BufferedReader(new FileReader("/Users/Passionate/Desktop/test_std.arff"))); 3. filteredData.setClassIndex(filteredData.attribute("@@class@@").index()); 4. Classifier classifier=new SMO(); 5. classifier.buildClassifier(filteredData); 6. FilteredClassifier filteredClassifier=new FilteredClassifier(); 7. filteredClassifier.setClassifier(classifier); 8. Evaluation eval = new Evaluation(filteredData); 9. eval.evaluateModel(filteredClassifier, filteredTests); **// Error line.** 10. System.out.println(eval.toSummaryString("\nResults\n======\n", false));
protected void buildInternal(MultiLabelInstances train) throws Exception { debug("Transforming the training set"); Instances meta = transformation.transformInstances(train); baseClassifier.buildClassifier(meta); header = new Instances(meta, 0); }
public void buildClassifier(Dataset data) { utils = new ToWekaUtils(data); Instances inst = utils.getDataset(); try { wekaClass.buildClassifier(inst); } catch (Exception e) { throw new WekaException(e); } }
@Override public void buildClassifier(Instances D) throws Exception { testCapabilities(D); for (int i = 0; i < D.numInstances(); i++) { m_Count.put(MLUtils.toBitString(D.instance(i),D.classIndex()),0); } m_Classifier.buildClassifier(D); }
@Override public void buildClassifier(Instances D) throws Exception { testCapabilities(D); for (int i = 0; i < D.numInstances(); i++) { m_Count.put(MLUtils.toBitString(D.instance(i),D.classIndex()),0); } m_Classifier.buildClassifier(D); }
@Override public void buildClassifier(Instances D) throws Exception { testCapabilities(D); for (int i = 0; i < D.numInstances(); i++) { m_Count.put(MLUtils.toBitString(D.instance(i),D.classIndex()),0); } m_Classifier.buildClassifier(D); }
@Override public void buildInternal(MultiLabelInstances mlData) throws Exception { //Do the transformation //and generate the classifier pt6Trans = new IncludeLabelsTransformation(); debug("Transforming the dataset"); transformed = pt6Trans.transformInstances(mlData); debug("Building the base-level classifier"); baseClassifier.buildClassifier(transformed); transformed.delete(); }
@Override protected void buildInternal(MultiLabelInstances trainingData) throws Exception { debug("building meta-model"); classifierInstances = transformData(trainingData); classifier.buildClassifier(classifierInstances); // keep just the header information classifierInstances = new Instances(classifierInstances, 0); debug("building the multi-label classifier"); baseLearner.setDebug(getDebug()); baseLearner.build(trainingData); } }
protected void buildInternal(MultiLabelInstances mlData) throws Exception { Instances transformedData; transformation = new LabelPowersetTransformation(); debug("Transforming the training set."); transformedData = transformation.transformInstances(mlData); //debug("Transformed training set: \n + transformedData.toString()); // check for unary class debug("Building single-label classifier."); if (transformedData.attribute(transformedData.numAttributes() - 1).numValues() > 1) { baseClassifier.buildClassifier(transformedData); } }