public Classifier train(Instances data, File model, List<String> parameters) throws Exception { List<String> mlArgs = parameters.subList(1, parameters.size()); MultiLabelClassifier cl = (MultiLabelClassifier) AbstractClassifier .forName((String) parameters.get(0), new String[] {}); if (!mlArgs.isEmpty()) { cl.setOptions(mlArgs.toArray(new String[0])); } cl.buildClassifier(data); if (serializeModel) { weka.core.SerializationHelper.write(model.getAbsolutePath(), cl); } return cl; }
public Classifier train(Instances data, File model, List<String> parameters) throws Exception { List<String> mlArgs = parameters.subList(1, parameters.size()); MultiLabelClassifier cl = (MultiLabelClassifier) AbstractClassifier .forName((String) parameters.get(0), new String[] {}); if (!mlArgs.isEmpty()) { cl.setOptions(mlArgs.toArray(new String[0])); } cl.buildClassifier(data); if (serializeModel) { weka.core.SerializationHelper.write(model.getAbsolutePath(), cl); } return cl; }
@Override public void buildClassifier(Instances train) throws Exception { testCapabilities(train); if (getDebug()) System.out.print("-: Models: "); train = new Instances(train); m_Classifiers = ProblemTransformationMethod.makeCopies((ProblemTransformationMethod) m_Classifier, m_NumIterations); int sub_size = (train.numInstances()*m_BagSizePercent/100); for(int i = 0; i < m_NumIterations; i++) { if(getDebug()) System.out.print(""+i+" "); if (m_Classifiers[i] instanceof Randomizable) ((Randomizable)m_Classifiers[i]).setSeed(i); train.randomize(new Random(m_Seed+i)); Instances sub_train = new Instances(train,0,sub_size); m_Classifiers[i].buildClassifier(sub_train); } if (getDebug()) System.out.println(":-"); }
@Override public void buildClassifier(Instances train) throws Exception { testCapabilities(train); if (getDebug()) System.out.print("-: Models: "); train = new Instances(train); m_Classifiers = ProblemTransformationMethod.makeCopies((ProblemTransformationMethod) m_Classifier, m_NumIterations); int sub_size = (train.numInstances()*m_BagSizePercent/100); for(int i = 0; i < m_NumIterations; i++) { if(getDebug()) System.out.print(""+i+" "); if (m_Classifiers[i] instanceof Randomizable) ((Randomizable)m_Classifiers[i]).setSeed(i); train.randomize(new Random(m_Seed+i)); Instances sub_train = new Instances(train,0,sub_size); m_Classifiers[i].buildClassifier(sub_train); } if (getDebug()) System.out.println(":-"); }
@Override public void buildClassifier(Instances train) throws Exception { testCapabilities(train); if (getDebug()) System.out.print("-: Models: "); //m_Classifiers = (MultilabelClassifier[]) AbstractClassifier.makeCopies(m_Classifier, m_NumIterations); m_Classifiers = ProblemTransformationMethod.makeCopies((ProblemTransformationMethod) m_Classifier, m_NumIterations); for(int i = 0; i < m_NumIterations; i++) { Random r = new Random(m_Seed+i); Instances bag = new Instances(train,0); if (m_Classifiers[i] instanceof Randomizable) ((Randomizable)m_Classifiers[i]).setSeed(m_Seed+i); if(getDebug()) System.out.print(""+i+" "); int bag_no = (m_BagSizePercent*train.numInstances()/100); //System.out.println(" bag no: "+bag_no); while(bag.numInstances() < bag_no) { bag.add(train.instance(r.nextInt(train.numInstances()))); } m_Classifiers[i].buildClassifier(bag); } if (getDebug()) System.out.println(":-"); }
@Override public void buildClassifier(Instances train) throws Exception { testCapabilities(train); if (getDebug()) System.out.print("-: Models: "); //m_Classifiers = (MultilabelClassifier[]) AbstractClassifier.makeCopies(m_Classifier, m_NumIterations); m_Classifiers = ProblemTransformationMethod.makeCopies((ProblemTransformationMethod) m_Classifier, m_NumIterations); for(int i = 0; i < m_NumIterations; i++) { Random r = new Random(m_Seed+i); Instances bag = new Instances(train,0); if (m_Classifiers[i] instanceof Randomizable) ((Randomizable)m_Classifiers[i]).setSeed(m_Seed+i); if(getDebug()) System.out.print(""+i+" "); int bag_no = (m_BagSizePercent*train.numInstances()/100); //System.out.println(" bag no: "+bag_no); while(bag.numInstances() < bag_no) { bag.add(train.instance(r.nextInt(train.numInstances()))); } m_Classifiers[i].buildClassifier(bag); } if (getDebug()) System.out.println(":-"); }
@Override public void buildClassifier(Instances train) throws Exception { testCapabilities(train); if (getDebug()) System.out.print("-: Models: "); train = new Instances(train); m_Classifiers = ProblemTransformationMethod.makeCopies((MultiLabelClassifier) m_Classifier, m_NumIterations); for(int i = 0; i < m_NumIterations; i++) { Random r = new Random(m_Seed+i); Instances bag = new Instances(train,0); if (m_Classifiers[i] instanceof Randomizable) ((Randomizable)m_Classifiers[i]).setSeed(m_Seed+i); if(getDebug()) System.out.print(""+i+" "); int ixs[] = new int[train.numInstances()]; for(int j = 0; j < ixs.length; j++) { ixs[r.nextInt(ixs.length)]++; } for(int j = 0; j < ixs.length; j++) { if (ixs[j] > 0) { Instance instance = train.instance(j); instance.setWeight(ixs[j]); bag.add(instance); } } m_Classifiers[i].buildClassifier(bag); } if (getDebug()) System.out.println(":-"); }
@Override public void buildClassifier(Instances train) throws Exception { testCapabilities(train); if (getDebug()) System.out.print("-: Models: "); train = new Instances(train); m_Classifiers = ProblemTransformationMethod.makeCopies((MultiLabelClassifier) m_Classifier, m_NumIterations); for(int i = 0; i < m_NumIterations; i++) { Random r = new Random(m_Seed+i); Instances bag = new Instances(train,0); if (m_Classifiers[i] instanceof Randomizable) ((Randomizable)m_Classifiers[i]).setSeed(m_Seed+i); if(getDebug()) System.out.print(""+i+" "); int ixs[] = new int[train.numInstances()]; for(int j = 0; j < ixs.length; j++) { ixs[r.nextInt(ixs.length)]++; } for(int j = 0; j < ixs.length; j++) { if (ixs[j] > 0) { Instance instance = train.instance(j); instance.setWeight(ixs[j]); bag.add(instance); } } m_Classifiers[i].buildClassifier(bag); } if (getDebug()) System.out.println(":-"); }
h.buildClassifier(D_init); // initial classifier train_time = System.currentTimeMillis() - train_time; if (h.getDebug()) {
h.buildClassifier(D_init); // initial classifier train_time = System.currentTimeMillis() - train_time; if (h.getDebug()) {
classifier.buildClassifier(m_Train); eval = Evaluation.evaluateModel(classifier, m_Train, m_TOP, m_VOP); classifier.buildClassifier(m_Train); eval = Evaluation.evaluateModel(classifier, m_Test, m_TOP, m_VOP);
classifier.buildClassifier(m_Train); eval = Evaluation.evaluateModel(classifier, m_Train, m_TOP, m_VOP); classifier.buildClassifier(m_Train); eval = Evaluation.evaluateModel(classifier, m_Test, m_TOP, m_VOP);
if(getDebug()) System.out.println("."); m_Classifiers[i].buildClassifier(D_cut); m_InstanceTemplates[i] = D_cut.instance(1); m_InstancesTemplates[i] = new Instances(D_cut,0);
if(getDebug()) System.out.println("."); m_Classifiers[i].buildClassifier(D_cut); m_InstanceTemplates[i] = D_cut.instance(1); m_InstancesTemplates[i] = new Instances(D_cut,0);
h.buildClassifier(D_train); h.buildClassifier(D_full);
h.buildClassifier(D_train); h.buildClassifier(D_full);