Implement an NB-tree in java with explanation.program must compile. Solution pac

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Implement an NB-tree in java with explanation.program must compile.

Explanation / Answer

package weka.classifiers.trees; import java.util.Enumeration; import java.util.Vector; import weka.classifiers.AbstractClassifier; import weka.classifiers.trees.j48.NBTreeClassifierTree; import weka.classifiers.trees.j48.NBTreeModelSelection; import weka.core.AdditionalMeasureProducer; import weka.core.Capabilities; import weka.core.Drawable; import weka.core.Instance; import weka.core.Instances; import weka.core.RevisionUtils; import weka.core.Summarizable; import weka.core.TechnicalInformation; import weka.core.TechnicalInformationHandler; import weka.core.WeightedInstancesHandler; import weka.core.TechnicalInformation.Field; import weka.core.TechnicalInformation.Type; /** * Class for generating a decision tree with naive Bayes classifiers at the leaves.
*
* For more information, see
*
* Ron Kohavi: Scaling Up the Accuracy of Naive-Bayes Classifiers: A Decision-Tree Hybrid. In: Second International Conference on Knoledge Discovery and Data Mining, 202-207, 1996. *

* * BibTeX: *

  * @inproceedings{Kohavi1996,  *    author = {Ron Kohavi},  *    booktitle = {Second International Conference on Knoledge Discovery and Data Mining},  *    pages = {202-207},  *    title = {Scaling Up the Accuracy of Naive-Bayes Classifiers: A Decision-Tree Hybrid},  *    year = {1996}  * }  * 

*

* * Valid options are:

* *

 -D  *  If set, classifier is run in debug mode and  *  may output additional info to the console

* * * @author Mark Hall * @version $Revision: 5928 $ */ public class NBTree extends AbstractClassifier implements WeightedInstancesHandler, Drawable, Summarizable, AdditionalMeasureProducer, TechnicalInformationHandler { /** for serialization */ static final long serialVersionUID = -4716005707058256086L; /** Minimum number of instances */ private int m_minNumObj = 30; /** The root of the tree */ private NBTreeClassifierTree m_root; /** * Returns a string describing classifier * @return a description suitable for * displaying in the explorer/experimenter gui */ public String globalInfo() { return "Class for generating a decision tree with naive Bayes classifiers at " + "the leaves. " + "For more information, see " + getTechnicalInformation().toString(); } /** * Returns an instance of a TechnicalInformation object, containing * detailed information about the technical background of this class, * e.g., paper reference or book this class is based on. * * @return the technical information about this class */ public TechnicalInformation getTechnicalInformation() { TechnicalInformation result; result = new TechnicalInformation(Type.INPROCEEDINGS); result.setValue(Field.AUTHOR, "Ron Kohavi"); result.setValue(Field.TITLE, "Scaling Up the Accuracy of Naive-Bayes Classifiers: A Decision-Tree Hybrid"); result.setValue(Field.BOOKTITLE, "Second International Conference on Knoledge Discovery and Data Mining"); result.setValue(Field.YEAR, "1996"); result.setValue(Field.PAGES, "202-207"); return result; } /** * Returns default capabilities of the classifier. * * @return the capabilities of this classifier */ public Capabilities getCapabilities() { return new NBTreeClassifierTree(null).getCapabilities(); } /** * Generates the classifier. * * @param instances the data to train with * @throws Exception if classifier can't be built successfully */ public void buildClassifier(Instances instances) throws Exception { NBTreeModelSelection modSelection = new NBTreeModelSelection(m_minNumObj, instances); m_root = new NBTreeClassifierTree(modSelection); m_root.buildClassifier(instances); } /** * Classifies an instance. * * @param instance the instance to classify * @return the classification * @throws Exception if instance can't be classified successfully */ public double classifyInstance(Instance instance) throws Exception { return m_root.classifyInstance(instance); } /** * Returns class probabilities for an instance. * * @param instance the instance to get the distribution for * @return the class probabilities * @throws Exception if distribution can't be computed successfully */ public final double[] distributionForInstance(Instance instance) throws Exception { return m_root.distributionForInstance(instance, false); } /** * Returns a description of the classifier. * * @return a string representation of the classifier */ public String toString() { if (m_root == null) { return "No classifier built"; } return "NBTree ------------------ " + m_root.toString(); } /** * Returns the type of graph this classifier * represents. * @return Drawable.TREE */ public int graphType() { return Drawable.TREE; } /** * Returns graph describing the tree. * * @return the graph describing the tree * @throws Exception if graph can't be computed */ public String graph() throws Exception { return m_root.graph(); } /** * Returns a superconcise version of the model * * @return a description of the model */ public String toSummaryString() { return "Number of leaves: " + m_root.numLeaves() + " " + "Size of the tree: " + m_root.numNodes() + " "; } /** * Returns the size of the tree * @return the size of the tree */ public double measureTreeSize() { return m_root.numNodes(); } /** * Returns the number of leaves * @return the number of leaves */ public double measureNumLeaves() { return m_root.numLeaves(); } /** * Returns the number of rules (same as number of leaves) * @return the number of rules */ public double measureNumRules() { return m_root.numLeaves(); } /** * Returns the value of the named measure * @param additionalMeasureName the name of the measure to query for its value * @return the value of the named measure * @throws IllegalArgumentException if the named measure is not supported */ public double getMeasure(String additionalMeasureName) { if (additionalMeasureName.compareToIgnoreCase("measureNumRules") == 0) { return measureNumRules(); } else if (additionalMeasureName.compareToIgnoreCase("measureTreeSize") == 0) { return measureTreeSize(); } else if (additionalMeasureName.compareToIgnoreCase("measureNumLeaves") == 0) { return measureNumLeaves(); } else { throw new IllegalArgumentException(additionalMeasureName + " not supported (j48)"); } } /** * Returns an enumeration of the additional measure names * @return an enumeration of the measure names */ public Enumeration enumerateMeasures() { Vector newVector = new Vector(3); newVector.addElement("measureTreeSize"); newVector.addElement("measureNumLeaves"); newVector.addElement("measureNumRules"); return newVector.elements(); } /** * Returns the revision string. * * @return the revision */ public String getRevision() { return RevisionUtils.extract("$Revision: 5928 $"); } /** * Main method for testing this class * * @param argv the commandline options */ public static void main(String[] argv){ runClassifier(new NBTree(), argv); } }

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