GrowTree.java Source code

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Here is the source code for GrowTree.java

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/*
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/**
 *
 * @author Venkatesh
 */
import weka.core.Attribute;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.matrix.Matrix;
import weka.core.Capabilities;
import weka.core.Capabilities.Capability;
import weka.classifiers.RandomizableClassifier;
import weka.core.converters.ConverterUtils.DataSource;
import weka.classifiers.Evaluation;
import java.util.Random;

import java.util.Arrays;
import java.util.Enumeration;
import java.util.Random;
import java.util.Vector;

public class GrowTree extends RandomizableClassifier {
    double distribution[];

    public static void main(String[] args) throws Exception {
        runClassifier(new GrowTree(), args);
        DataSource source = new DataSource(
                "F:\\backup\\BTH\\#6DV2542 Machine Learning\\WEKA experiments\\UCI\\iris.arff");
        Instances data = source.getDataSet();
        if (data.classIndex() == -1)
            data.setClassIndex(data.numAttributes() - 1);
    }

    public void buildClassifier(Instances data) throws Exception {
        growTree(data);
    }

    public boolean homogeneous(Instances D) {
        distribution = new double[D.numClasses()];
        Enumeration eninst = D.enumerateInstances();
        while (eninst.hasMoreElements()) {
            Instance ele = (Instance) eninst.nextElement();
            distribution[(int) ele.classValue()]++;
        }

        int cnt = 0;
        for (int i = 0; i < D.numClasses(); i++) {
            if (distribution[i] > 0)
                cnt++;
        }
        if (cnt <= 1) // if all instances are of single class
            return true;
        else
            return false;
    }

    double label(Instances D) {
        Enumeration eninst = D.enumerateInstances();

        Instance ele = (Instance) eninst.nextElement();
        return ele.classValue();
    }

    Attribute bestSplit(Instances D) {
        double imin = 1.0;
        Attribute fbest = null;
        Enumeration enat = D.enumerateAttributes();
        while (enat.hasMoreElements()) {
            Attribute a = (Attribute) enat.nextElement();
            //split D into subsets d1 to dn based on values vi based on features
            Instances[] split = new Instances[a.numValues()];
            for (int i = 0; i < a.numValues(); i++) {
                split[i] = new Instances(D, D.numInstances());
            }
            Enumeration x = D.enumerateInstances();
            while (x.hasMoreElements()) {
                Instance in = (Instance) x.nextElement();
                split[(int) in.value(a)].add(in);
            }
            for (int i = 0; i < split.length; i++) {
                split[i].compactify();
            }
            for (int i = 0; i < a.numValues(); i++) {
                if (imp(split[i]) < imin) {
                    imin = imp(split[i]);
                    fbest = a; //evaluate the best feature to make root
                }
            }
        }
        return fbest;

    }

Tree growTree(Instances D)
{
    if(homogeneous(D))
        return label(D);
        
    Attribute S=bestSplit(D);
        
    //split D into D1...Dn
    Instances[] split = new Instances[S.numValues()];
    for (int i = 0; i < S.numValues(); i++) 
    {
        split[i] = new Instances(D, D.numInstances());
    }
    Enumeration x= D.enumerateInstances();
    while (x.hasMoreElements()) 
    {
        Instance in = (Instance) x.nextElement();
        split[(int) in.value(S)].add(in);
    }
    for (int i = 0; i < split.length; i++) 
    {
        split[i].compactify();
    }
    for(int i=0;i<S.numValues();i++)
    {
        if(split[i]!=null)
            Ti=growTree(split[i]);
        else
            return label(split[i]);
    }
    return tree whose root at S and children Di;
        
}

    public void crossvalidaion() throws Exception {
        DataSource source = new DataSource(
                "F:\\backup\\BTH\\#6DV2542 Machine Learning\\WEKA experiments\\UCI\\iris.arff");
        Instances newData = source.getDataSet();
        Evaluation eval = new Evaluation(newData);
        eval.crossValidateModel(tree, newData, 10, new Random(1));
        System.out.println(eval.toSummaryString("\nResults\n======\n", false));
    }

    public double imp(Instances data) {
        double localdistribution[] = new double[data.numClasses()];
        Enumeration eninst = data.enumerateInstances();
        while (eninst.hasMoreElements()) {
            Instance ele = (Instance) eninst.nextElement();
            localdistribution[(int) ele.classValue()]++;
        }

        return imp;
    }

}