public static void main(String args[]) { ProblemTransformationMethod.evaluation(new CDN(), args); }
/** * Description to display in the GUI. * * @return the description */ @Override public String globalInfo() { return "A Conditional Dependency Network. " + "For more information see:\n" + getTechnicalInformation().toString(); }
@Override public Enumeration listOptions() { Vector result = new Vector(); result.addElement(new Option("\t"+iTipText()+"\n\tdefault: 1000", "I", 1, "-I <value>")); result.addElement(new Option("\t"+icTipText()+"\n\tdefault: 100", "Ic", 1, "-Ic <value>")); result.addElement(new Option("\t"+seedTipText(), "S", 1, "-S <value>")); OptionUtils.add(result, super.listOptions()); return OptionUtils.toEnumeration(result); }
@Override public void buildClassifier(Instances D) throws Exception { testCapabilities(D); int N = D.numInstances(); int L = D.classIndex(); h = new Classifier[L]; m_R = new Random(m_S); D_templates = new Instances[L]; // Build L probabilistic models, each to predict Y_i | X, Y_{-y}; save the templates. for(int j = 0; j < L; j++) { // X = [Y[0],...,Y[j-1],Y[j+1],...,Y[L],X] D_templates[j] = new Instances(D); D_templates[j].setClassIndex(j); // train H[j] : X -> Y h[j] = AbstractClassifier.forName(getClassifier().getClass().getName(),((AbstractClassifier)getClassifier()).getOptions()); h[j].buildClassifier(D_templates[j]); } }
@Override public String [] getOptions() { List<String> result = new ArrayList<>(); OptionUtils.add(result, 'H', getWidth()); OptionUtils.add(result, 'L', getDensity()); OptionUtils.add(result, 'X', getDependencyMetric()); OptionUtils.add(result, super.getOptions()); return OptionUtils.toArray(result); }
@Override public Enumeration listOptions() { Vector result = new Vector(); result.addElement(new Option("\t"+widthTipText(), "H", 1, "-H <value>")); result.addElement(new Option("\t"+densityTipText(), "L", 1, "-L <value>")); result.addElement(new Option("\t"+dependencyMetricTipText(), "X", 1, "-X <value>")); OptionUtils.add(result, super.listOptions()); return OptionUtils.toEnumeration(result); }
@Override public Enumeration listOptions() { Vector result = new Vector(); result.addElement(new Option("\t"+iTipText()+"\n\tdefault: 1000", "I", 1, "-I <value>")); result.addElement(new Option("\t"+icTipText()+"\n\tdefault: 100", "Ic", 1, "-Ic <value>")); result.addElement(new Option("\t"+seedTipText(), "S", 1, "-S <value>")); OptionUtils.add(result, super.listOptions()); return OptionUtils.toEnumeration(result); }
@Override public void buildClassifier(Instances D) throws Exception { testCapabilities(D); int N = D.numInstances(); int L = D.classIndex(); h = new Classifier[L]; m_R = new Random(m_S); D_templates = new Instances[L]; // Build L probabilistic models, each to predict Y_i | X, Y_{-y}; save the templates. for(int j = 0; j < L; j++) { // X = [Y[0],...,Y[j-1],Y[j+1],...,Y[L],X] D_templates[j] = new Instances(D); D_templates[j].setClassIndex(j); // train H[j] : X -> Y h[j] = AbstractClassifier.forName(getClassifier().getClass().getName(),((AbstractClassifier)getClassifier()).getOptions()); h[j].buildClassifier(D_templates[j]); } }
@Override public String [] getOptions() { List<String> result = new ArrayList<>(); OptionUtils.add(result, 'H', getWidth()); OptionUtils.add(result, 'L', getDensity()); OptionUtils.add(result, 'X', getDependencyMetric()); OptionUtils.add(result, super.getOptions()); return OptionUtils.toArray(result); }
@Override public Enumeration listOptions() { Vector result = new Vector(); result.addElement(new Option("\t"+widthTipText(), "H", 1, "-H <value>")); result.addElement(new Option("\t"+densityTipText(), "L", 1, "-L <value>")); result.addElement(new Option("\t"+dependencyMetricTipText(), "X", 1, "-X <value>")); OptionUtils.add(result, super.listOptions()); return OptionUtils.toEnumeration(result); }
/** * Description to display in the GUI. * * @return the description */ @Override public String globalInfo() { return "A Conditional Dependency Network. " + "For more information see:\n" + getTechnicalInformation().toString(); }
public static void main(String args[]) { ProblemTransformationMethod.evaluation(new CDN(), args); }