public MBR() { // default classifier for GUI this.m_Classifier = new BR(); }
protected Classifier getDefaultClassifier() { return new BR(); }
public MBR() { // default classifier for GUI this.m_Classifier = new BR(); }
public DeepML() { // default classifier for GUI this.m_Classifier = new BR(); }
public SubsetMapper() { // default classifier for GUI this.m_Classifier = new BR(); }
public SubsetMapper() { // default classifier for GUI this.m_Classifier = new BR(); }
public DeepML() { // default classifier for GUI this.m_Classifier = new BR(); }
protected Classifier getDefaultClassifier() { return new BR(); }
public static void main(String args[]) { ProblemTransformationMethod.evaluation(new BR(), args); }
public static void main(String args[]) { ProblemTransformationMethod.evaluation(new BR(), args); }
public static void main(String[] args) throws Exception { if (args.length != 1) throw new IllegalArgumentException("Required arguments: <dataset>"); System.out.println("Loading data: " + args[0]); Instances data = DataSource.read(args[0]); MLUtils.prepareData(data); System.out.println("Build BR classifier"); BR classifier = new BR(); // further configuration of classifier classifier.buildClassifier(data); } }
/** * Default constructor. * * Turns off check for modified class attribute. */ public FilteredClassifier() { super(); setDoNotCheckForModifiedClassAttribute(true); m_Classifier = new BR(); m_Filter = new AllFilter(); }
/** * Default constructor. * * Turns off check for modified class attribute. */ public FilteredClassifier() { super(); setDoNotCheckForModifiedClassAttribute(true); m_Classifier = new BR(); m_Filter = new AllFilter(); }
public static void main(String[] args) throws Exception { if (args.length != 1) throw new IllegalArgumentException("Required arguments: <dataset>"); System.out.println("Loading data: " + args[0]); Instances data = DataSource.read(args[0]); MLUtils.prepareData(data); int numFolds = 10; System.out.println("Cross-validate BR classifier using " + numFolds + " folds"); BR classifier = new BR(); // further configuration of classifier String top = "PCut1"; String vop = "3"; Result result = Evaluation.cvModel(classifier, data, numFolds, top, vop); System.out.println(result); } }
/** * Lets the user add a classifier. */ protected void addClassifier() { GenericObjectEditorDialog dialog; dialog = getGOEDialog(MultiLabelClassifier.class, new BR()); dialog.setTitle("Add classifier"); dialog.setLocationRelativeTo(this); dialog.setVisible(true); if (dialog.getResult() != GenericObjectEditorDialog.APPROVE_OPTION) return; if (m_ListClassifiers.getList().getSelectedIndex() > -1) m_ModelClassifiers.insertElementAt(OptionUtils.toCommandLine(dialog.getCurrent()), m_ListClassifiers.getList().getSelectedIndex()); else m_ModelClassifiers.addElement(OptionUtils.toCommandLine(dialog.getCurrent())); m_Modified = true; updateButtons(); }
/** * For testing only. * * @param args ignored */ public static void main(String[] args) { GenericObjectEditorDialog dialog = new GenericObjectEditorDialog((Frame) null, "Object editor", true); dialog.setDefaultCloseOperation(GenericObjectEditorDialog.DISPOSE_ON_CLOSE); dialog.getGOEEditor().setClassType(meka.classifiers.multilabel.MultiLabelClassifier.class); dialog.getGOEEditor().setCanChangeClassInDialog(true); dialog.setCurrent(new meka.classifiers.multilabel.BR()); dialog.setLocationRelativeTo(null); dialog.setVisible(true); if (dialog.getResult() == APPROVE_OPTION) System.out.println(dialog.getCurrent()); } }
public static double[][] LEAD(Instances D, Classifier h, Random r, String MDType) throws Exception { Instances D_r = new Instances(D); D_r.randomize(r); Instances D_train = new Instances(D_r,0,D_r.numInstances()*60/100); Instances D_test = new Instances(D_r,D_train.numInstances(),D_r.numInstances()-D_train.numInstances()); BR br = new BR(); br.setClassifier(h); Result result = Evaluation.evaluateModel((MultiLabelClassifier)br,D_train,D_test,"PCut1","1"); return LEAD(D_test, result, MDType); }
public static double[][] LEAD(Instances D, Classifier h, Random r, String MDType) throws Exception { Instances D_r = new Instances(D); D_r.randomize(r); Instances D_train = new Instances(D_r,0,D_r.numInstances()*60/100); Instances D_test = new Instances(D_r,D_train.numInstances(),D_r.numInstances()-D_train.numInstances()); BR br = new BR(); br.setClassifier(h); Result result = Evaluation.evaluateModel((MultiLabelClassifier)br,D_train,D_test,"PCut1","1"); return LEAD(D_test, result, MDType); }
/** * LEAD - Performs LEAD on dataset 'D', using BR with base classifier 'h', under random seed 'r'. * <br> * WARNING: changing this method will affect the perfomance of e.g., BCC -- on the other hand the original BCC paper did not use LEAD, so don't worry. */ public static double[][] LEAD(Instances D, Classifier h, Random r) throws Exception { Instances D_r = new Instances(D); D_r.randomize(r); Instances D_train = new Instances(D_r,0,D_r.numInstances()*60/100); Instances D_test = new Instances(D_r,D_train.numInstances(),D_r.numInstances()-D_train.numInstances()); BR br = new BR(); br.setClassifier(h); Result result = Evaluation.evaluateModel((MultiLabelClassifier)br,D_train,D_test,"PCut1","1"); return LEAD2(D_test,result); }
/** * LEAD - Performs LEAD on dataset 'D', using BR with base classifier 'h', under random seed 'r'. * <br> * WARNING: changing this method will affect the perfomance of e.g., BCC -- on the other hand the original BCC paper did not use LEAD, so don't worry. */ public static double[][] LEAD(Instances D, Classifier h, Random r) throws Exception { Instances D_r = new Instances(D); D_r.randomize(r); Instances D_train = new Instances(D_r,0,D_r.numInstances()*60/100); Instances D_test = new Instances(D_r,D_train.numInstances(),D_r.numInstances()-D_train.numInstances()); BR br = new BR(); br.setClassifier(h); Result result = Evaluation.evaluateModel((MultiLabelClassifier)br,D_train,D_test,"PCut1","1"); return LEAD2(D_test,result); }