public CRUpdateable() { // default classifier for GUI this.m_Classifier = new HoeffdingTree(); }
public static void main(String[] args) { runClassifier(new HoeffdingTree(), args); }
/** * Gets the current settings of the Classifier. * * @return an array of strings suitable for passing to setOptions */ @Override public String[] getOptions() { ArrayList<String> options = new ArrayList<String>(); options.add("-L"); options.add("" + getLeafPredictionStrategy().getSelectedTag().getID()); options.add("-S"); options.add("" + getSplitCriterion().getSelectedTag().getID()); options.add("-E"); options.add("" + getSplitConfidence()); options.add("-H"); options.add("" + getHoeffdingTieThreshold()); options.add("-M"); options.add("" + getMinimumFractionOfWeightInfoGain()); options.add("-G"); options.add("" + getGracePeriod()); options.add("-N"); options.add("" + getNaiveBayesPredictionThreshold()); if (m_printLeafModels) { options.add("-P"); } return options.toArray(new String[1]); }
/** * Builds the classifier. * * @param data the data to train with * @throws Exception if classifier can't be built successfully */ @Override public void buildClassifier(Instances data) throws Exception { reset(); m_header = new Instances(data, 0); if (m_selectedSplitMetric == GINI_SPLIT) { m_splitMetric = new GiniSplitMetric(); } else { m_splitMetric = new InfoGainSplitMetric(m_minFracWeightForTwoBranchesGain); } data = new Instances(data); data.deleteWithMissingClass(); for (int i = 0; i < data.numInstances(); i++) { updateClassifier(data.instance(i)); } // can classifier handle the data? getCapabilities().testWithFail(data); }
double hoeffdingBound = computeHoeffdingBound(metricMax, m_splitConfidence, node.totalWeight()); deactivateNode(node, parent, parentBranch); } else { SplitNode newSplit = new SplitNode(node.m_classDistribution, ActiveHNode newChild = newLearningNode(); newChild.m_classDistribution = best.m_postSplitClassDistributions .get(i);
/** * Gets the current settings of the Classifier. * * @return an array of strings suitable for passing to setOptions */ @Override public String[] getOptions() { ArrayList<String> options = new ArrayList<String>(); options.add("-L"); options.add("" + getLeafPredictionStrategy().getSelectedTag().getID()); options.add("-S"); options.add("" + getSplitCriterion().getSelectedTag().getID()); options.add("-E"); options.add("" + getSplitConfidence()); options.add("-H"); options.add("" + getHoeffdingTieThreshold()); options.add("-M"); options.add("" + getMinimumFractionOfWeightInfoGain()); options.add("-G"); options.add("" + getGracePeriod()); options.add("-N"); options.add("" + getNaiveBayesPredictionThreshold()); if (m_printLeafModels) { options.add("-P"); } return options.toArray(new String[1]); }
public static void main(String[] args) { runClassifier(new HoeffdingTree(), args); }
/** * Builds the classifier. * * @param data the data to train with * @throws Exception if classifier can't be built successfully */ @Override public void buildClassifier(Instances data) throws Exception { reset(); m_header = new Instances(data, 0); if (m_selectedSplitMetric == GINI_SPLIT) { m_splitMetric = new GiniSplitMetric(); } else { m_splitMetric = new InfoGainSplitMetric(m_minFracWeightForTwoBranchesGain); } data = new Instances(data); data.deleteWithMissingClass(); for (int i = 0; i < data.numInstances(); i++) { updateClassifier(data.instance(i)); } // can classifier handle the data? getCapabilities().testWithFail(data); }
double hoeffdingBound = computeHoeffdingBound(metricMax, m_splitConfidence, node.totalWeight()); deactivateNode(node, parent, parentBranch); } else { SplitNode newSplit = new SplitNode(node.m_classDistribution, ActiveHNode newChild = newLearningNode(); newChild.m_classDistribution = best.m_postSplitClassDistributions .get(i);
public CCUpdateable() { // default classifier for GUI this.m_Classifier = new HoeffdingTree(); }
public BRUpdateable() { // default classifier for GUI this.m_Classifier = new HoeffdingTree(); }
public CCUpdateable() { // default classifier for GUI this.m_Classifier = new HoeffdingTree(); }
public PSUpdateable() { // default classifier for GUI this.m_Classifier = new HoeffdingTree(); }
public CRUpdateable() { // default classifier for GUI this.m_Classifier = new HoeffdingTree(); }
public RTUpdateable() { // default classifier for GUI this.m_Classifier = new HoeffdingTree(); }
public PSUpdateable() { // default classifier for GUI this.m_Classifier = new HoeffdingTree(); }
public RTUpdateable() { // default classifier for GUI this.m_Classifier = new HoeffdingTree(); }
public BRUpdateable() { // default classifier for GUI this.m_Classifier = new HoeffdingTree(); }
/** Creates a default HoeffdingTree */ public Classifier getClassifier() { return new HoeffdingTree(); }
/** Creates a default HoeffdingTree */ public Classifier getClassifier() { return new HoeffdingTree(); }