public static void main(String args[]) { ProblemTransformationMethod.evaluation(new CCq(), args); } }
@Override public void buildClassifier(Instances Train) throws Exception { testCapabilities(Train); this.m_NumClasses = Train.classIndex(); int indices[] = MLUtils.gen_indices(m_NumClasses); MLUtils.randomize(indices,new Random(m_S)); if(getDebug()) System.out.print(":- Chain ("); root = new QLink(indices,0,Train); if (getDebug()) System.out.println(" ) -:"); }
/** * Description to display in the GUI. * * @return the description */ @Override public String globalInfo() { return "The Classifier Chains Method - Random Subspace ('quick') Version.\n" + "This version is able to downsample the number of training instances across the binary models." + "For more information see:\n" + getTechnicalInformation().toString(); }
@Override public void setOptions(String[] options) throws Exception { setDownSampleRatio(OptionUtils.parse(options, 'P', 0.75)); setSeed(OptionUtils.parse(options, 'S', 0)); super.setOptions(options); }
@Override public String [] getOptions() { List<String> result = new ArrayList<>(); OptionUtils.add(result, 'P', getDownSampleRatio()); OptionUtils.add(result, 'S', getSeed()); OptionUtils.add(result, super.getOptions()); return OptionUtils.toArray(result); }
this.classifier = AbstractClassifier.forName(getClassifier().getClass().getName(),((AbstractClassifier)getClassifier()).getOptions()); if(getDebug()) System.out.print(" "+this.index); new_train.setClassIndex(-1);
@Override public void setOptions(String[] options) throws Exception { setDownSampleRatio(OptionUtils.parse(options, 'P', 0.75)); setSeed(OptionUtils.parse(options, 'S', 0)); super.setOptions(options); }
@Override public String [] getOptions() { List<String> result = new ArrayList<>(); OptionUtils.add(result, 'P', getDownSampleRatio()); OptionUtils.add(result, 'S', getSeed()); OptionUtils.add(result, super.getOptions()); return OptionUtils.toArray(result); }
this.classifier = AbstractClassifier.forName(getClassifier().getClass().getName(),((AbstractClassifier)getClassifier()).getOptions()); if(getDebug()) System.out.print(" "+this.index); new_train.setClassIndex(-1);
@Override public void buildClassifier(Instances Train) throws Exception { testCapabilities(Train); this.m_NumClasses = Train.classIndex(); int indices[] = MLUtils.gen_indices(m_NumClasses); MLUtils.randomize(indices,new Random(m_S)); if(getDebug()) System.out.print(":- Chain ("); root = new QLink(indices,0,Train); if (getDebug()) System.out.println(" ) -:"); }
public static void main(String args[]) { ProblemTransformationMethod.evaluation(new CCq(), args); } }
/** * Description to display in the GUI. * * @return the description */ @Override public String globalInfo() { return "The Classifier Chains Method - Random Subspace ('quick') Version.\n" + "This version is able to downsample the number of training instances across the binary models." + "For more information see:\n" + getTechnicalInformation().toString(); }